Applications of Evolutionary Computation
24th International Conference, EvoApplications 2021, Held as Part of EvoStar 2021, Virtual Event, April 7–9, 2021, Proceedings
- 2021
- Book
- Editors
- Pedro A. Castillo
- Juan Luis Jiménez Laredo
- Book Series
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
About this book
This book constitutes the refereed proceedings of the 24th International Conference on Applications of Evolutionary Computation, EvoApplications 2021, held as part of Evo*2021, as Virtual Event, in April 2021, co-located with the Evo*2021 events EuroGP, EvoCOP, and EvoMUSART.
The 51 revised full papers presented in this book were carefully reviewed and selected from 78 submissions. The papers cover a wide spectrum of topics, ranging from applications of evolutionary computation; applications of deep bioinspired algorithms; soft computing applied to games; machine learning and AI in digital healthcare and personalized medicine; evolutionary computation in image analysis, signal processing and pattern recognition; evolutionary machine learning; parallel and distributed systems; and applications of nature inspired computing for sustainability and development.
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Table of Contents
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Frontmatter
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Applications of Evolutionary Computation
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Frontmatter
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On Restricting Real-Valued Genotypes in Evolutionary Algorithms
Jørgen Nordmoen, Tønnes F. Nygaard, Eivind Samuelsen, Kyrre GletteThe chapter delves into the fundamental aspects of Evolutionary Algorithms (EAs) and the importance of restricting real-valued genotypes to task-specific bounds. It introduces the theoretical problem of value restriction and demonstrates how different restriction functions can significantly impact the distribution of values in the genome. Through empirical analysis, the authors show that the commonly used Clamped function skews the distribution towards the bounds, while the proposed Bounce-back function maintains a uniform distribution. The chapter also reviews the literature, revealing that many EA frameworks and practitioners overlook the critical role of restriction functions. By highlighting these issues and proposing a more effective solution, the authors aim to raise awareness and encourage further research in this area.AI Generated
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AbstractReal-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks. -
Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions
Quentin Renau, Johann Dreo, Carola Doerr, Benjamin DoerrThe chapter delves into the application of extreme feature selection for classifying BBOB functions, a crucial aspect of exploratory landscape analysis. By focusing on minimal feature sets, the authors address the critical issue of explainability in automated algorithm design. The study shows that small feature sets not only achieve high classification accuracy but also drastically reduce computational costs. The methodology is validated through extensive experiments, and the findings have significant implications for the efficiency and effectiveness of automated algorithm design processes.AI Generated
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AbstractFacilitated by the recent advances of Machine Learning (ML), the automated design of optimization heuristics is currently shaking up evolutionary computation (EC). Where the design of hand-picked guidelines for choosing a most suitable heuristic has long dominated research activities in the field, automatically trained heuristics are now seen to outperform human-derived choices even for well-researched optimization tasks. ML-based EC is therefore not any more a futuristic vision, but has become an integral part of our community.A key criticism that ML-based heuristics are often faced with is their potential lack of explainability, which may hinder future developments. This applies in particular to supervised learning techniques which extrapolate algorithms’ performance based on exploratory landscape analysis (ELA). In such applications, it is not uncommon to use dozens of problem features to build the models underlying the specific algorithm selection or configuration task. Our goal in this work is to analyze whether this many features are indeed needed. Using the classification of the BBOB test functions as testbed, we show that a surprisingly small number of features – often less than four – can suffice to achieve a 98% accuracy. Interestingly, the number of features required to meet this threshold is found to decrease with the problem dimension. We show that the classification accuracy transfers to settings in which several instances are involved in training and testing. In the leave-one-instance-out setting, however, classification accuracy drops significantly, and the transformation-invariance of the features becomes a decisive success factor. -
Co-optimising Robot Morphology and Controller in a Simulated Open-Ended Environment
Emma Hjellbrekke Stensby, Kai Olav Ellefsen, Kyrre GletteThe chapter delves into the challenge of co-optimising robot morphology and controller in unpredictable environments, highlighting the use of evolutionary algorithms to automate this process. It introduces the Paired Open-Ended Trailblazer (POET) algorithm, which evolves both environments and agents, and shows how POET can be modified to optimise robot morphologies. The study compares the performance and diversity of agents evolved with POET to those evolved in handcrafted curricula of environments, revealing that POET encourages morphological diversity and enables agents to generalise well to new environments. The findings suggest that POET could be a promising approach for tackling the stagnation problem in the co-optimisation of robot morphology and control.AI Generated
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AbstractDesigning robots by hand can be costly and time consuming, especially if the robots have to be created with novel materials, or be robust to internal or external changes. In order to create robots automatically, without the need for human intervention, it is necessary to optimise both the behaviour and the body design of the robot. However, when co-optimising the morphology and controller of a locomoting agent the morphology tends to converge prematurely, reaching a local optimum. Approaches such as explicit protection of morphological innovation have been used to reduce this problem, but it might also be possible to increase exploration of morphologies using a more indirect approach. We explore how changing the environment, where the agent locomotes, affects the convergence of morphologies. The agents’ morphologies and controllers are co-optimised, while the environments the agents locomote in are evolved open-endedly with the Paired Open-Ended Trailblazer (POET). We compare the diversity, fitness and robustness of agents evolving in environments generated by POET to agents evolved in handcrafted curricula of environments. Our agents each contain of a population of individuals being evolved with a genetic algorithm. This population is called the agent-population. We show that agent-populations evolving in open-endedly evolving environments exhibit larger morphological diversity than agent-populations evolving in hand crafted curricula of environments. POET proved capable of creating a curriculum of environments which encouraged both diversity and quality in the populations. This suggests that POET may be capable of reducing premature convergence in co-optimisation of morphology and controllers. -
Multi-objective Workforce Allocation in Construction Projects
Andrew Iskandar, Richard AllmendingerThis chapter delves into the complexities of construction project management, specifically focusing on the multi-objective workforce allocation problem. Using real data from construction projects, the authors simulate and address competing goals such as budget, cost, and quality targets. The core challenge is to decide the optimal allocation of labor with varying skill levels to minimize project duration, cost, and maximize quality. The chapter introduces a multi-objective evolutionary algorithm (MOEA) to tackle this problem, providing insights into the trade-offs between objectives and the impact of varying constraints. The results highlight significant improvements over traditional methods, offering practical implications for business decision-making and resource allocation strategies in construction projects.AI Generated
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AbstractManaging construction projects is a complex, resource-intense and risky task that involves the organization and management of people skilled in the design and completion of construction projects. Embarking on a construction project means to plan the allocation of resources and labour, while ensuring that the output (e.g. a new building) meets a certain quality, and is delivered in time and within budget without breaching contractual obligations. We formulate a simplified version of this task as a constrained multi-objective optimization problem, and then use a non-dominated sorting genetic algorithm to tackle the problem. In addition to providing a formal definition of the problem, further contributions of this work include the validation of the methodology using real data of construction projects varying in scale and resource-utilisation; the use of real data is scarce in the construction project management area. We also perform a scenario-based analysis to understand how the approach reacts to changing environmental parameters (such as availability of resources). Finally, we discuss practical implications. Our empirical analysis highlights that the proposed approach improves significantly in terms of project budget, quality, and duration targets, when compared with the industry standard. -
Generating Duplex Routes for Robust Bus Transport Network by Improved Multi-objective Evolutionary Algorithm Based on Decomposition
Sho Kajihara, Hiroyuki Sato, Keiki TakadamaThe chapter discusses the importance of bus transport networks, especially in disaster situations, and the challenges posed by environmental changes. It introduces the concept of 'duplex routes' as an alternative to traditional route modification methods. The authors propose an improved Multi-objective Evolutionary Algorithm based on Decomposition (MOEA/D) to generate these duplex routes, enhancing the network's resilience. The method includes a crossover operation to create alternative routes and a priority solution update to maintain route diversity, ensuring both robustness and efficiency. Experimental results validate the effectiveness of the proposed method in generating high-quality duplex routes, highlighting its potential for real-world applications.AI Generated
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AbstractThis paper proposes the duplex route generation method to evolve the bus route network which is robust to environmental changes and aims at investigating its effectiveness through the experiments. In this study, the “duplex route” corresponds to the alternative route and it has the advantage of not requiring to modify the route network in the environmental changes. To generate the duplex routes, this study employs MOEA/D as the base optimization method and introduces the following two operations in MOEA/D to increase the duplex routes while improving the fitness: (1) the crossover operation to generate the duplex routes, which is improved from the crossover operation in SEAMO2 [9] that evolves unique routes, and (2) the priority solution update operation in the enhanced MOEA/D [4] to maintain a diversity of the routes which contributes to improving the fitness. The experiments on Mandl’s benchmark problem has revealed: (1) the proposed crossover operation can generate many duplex networks as compared to the original crossover operation; (2) the priority solution update operation improves the fitness, i.e., a minimization of the passenger transportation time and the number of buses; and (3) integration of the two operations improves both the number of duplex routes and fitness, which is hard to be achieved by either operation. -
Combining Multi-objective Evolutionary Algorithms with Deep Generative Models Towards Focused Molecular Design
Tiago Sousa, João Correia, Vitor Pereira, Miguel RochaThe chapter discusses the challenges of traditional molecular design and introduces deep generative models as a promising approach. It explores various generative models, including Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and Variational Auto-Encoders (VAEs), and their applications in molecular generation. The text then delves into the advantages of combining these models with Evolutionary Algorithms (EAs) to enhance the diversity and effectiveness of molecule generation. The proposed framework, which uses a chemical VAE and a multi-objective evolutionary algorithm, is benchmarked against state-of-the-art methods in several case studies. The results demonstrate the framework's ability to optimize molecular properties and generate diverse, high-quality molecular leads. The chapter concludes with a discussion of future work and the potential applications of this approach in real drug discovery scenarios.AI Generated
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AbstractRecent advances in applying deep generative learning to molecular design have led to a large number of novel approaches to the targeted generation of molecules towards specific features and applications. In this work, we expand on the latent space navigation approach, where molecules are optimized by operating in their latent representation inside a deep auto-encoder, by introducing multi-objective evolutionary algorithms (MOEAs), and benchmarking the proposed framework on several objectives from recent literature. Using several case studies from literature, we show that our proposed method is capable of controlling abstract chemical properties, is competitive with other state-of-the-art methods and can perform relevant tasks such as optimizing a predefined molecule while maintaining a similarity threshold. Also, MOEAs allow to generate molecules with a good level of diversity, which is a desired feature. -
A Multi-objective Evolutionary Algorithm Approach for Optimizing Part Quality Aware Assembly Job Shop Scheduling Problems
Michael H. Prince, Kristian DeHaan, Daniel R. TauritzThe chapter addresses the complex challenge of scheduling the production of high-quality, low-quantity mechanical and electrical components using a multi-objective evolutionary algorithm. It introduces a new formulation of the Assembly Job Shop Scheduling Problem (AJSSP) that incorporates quality constraints, enabling the optimization of part quality, production time, and lead time. The authors propose a genetic programming approach to model quality transformations and a multi-objective evolutionary algorithm to optimize the scheduling and part selection process. The chapter highlights the real-world relevance of the problem and the effectiveness of the proposed method through extensive experiments and comparisons with baseline algorithms.AI Generated
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AbstractMotivated by a real-world application, we consider an Assembly Job Shop Scheduling Problem (AJSSP), with three objectives: product quality, product quantity, and first product lead time. Using real-world inspection data, we demonstrate the ability to model product quality transformations during assembly jobs via genetic programming by considering the quality attributes of subparts. We investigate integrating quality transformation models into an AJSSP. Through the use of the de facto standard multi-objective evolutionary algorithm, NSGA-II, and a novel genotype to handle the constraints, we describe an evolutionary approach to optimizing all stated objectives. This approach is empirically shown to outperform random search and hill climbing in both performance and usability metrics expected to be valuable to administrators involved in plant scheduling and operations. -
Evolutionary Grain-Mixing to Improve Profitability in Farming Winter Wheat
Md Asaduzzaman Noor, John W. SheppardThe chapter discusses the critical role of grain mixing in optimizing the profitability of winter wheat farming. It introduces the grain mixing problem, focusing on the use of evolutionary algorithms to determine optimal mixing strategies. The authors compare Genetic Algorithms and Differential Evolution with other methods, such as a greedy mixing strategy and no mixing, to demonstrate the potential benefits of investing in technology to track protein levels in wheat. The chapter presents a detailed methodology, experimental design, and results, highlighting the superior performance of evolutionary algorithms in maximizing profit. The study also includes a discussion of future work and conclusions, emphasizing the practical implications for farmers and the agricultural industry.AI Generated
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AbstractThis paper focuses on adapting and applying a genetic algorithm (GA) and differential evolution (DE) to solve the grain (wheat) mixing problem. The proposed algorithms explore a search space that aims at finding a quality mixing of wheat from grain bins that produce the maximum profit at a grain elevator. The experimental results demonstrate that mixing bins provide more profit than not mixing, and that the evolutionary approaches lead to consistently higher profits than the non-evolutionary methods. -
Automatic Modular Design of Behavior Trees for Robot Swarms with Communication Capabilites
Jonas Kuckling, Vincent van Pelt, Mauro BirattariThe chapter delves into the challenges and methods of designing control software for robot swarms, focusing on the automatic modular design approach called AutoMoDe. It introduces Cedrata, a new method that assembles modules into behavior trees, enabling two-way control transfers and improved human understandability. The chapter compares Cedrata with existing methods like Maple, showcasing its effectiveness through experiments on three missions: Foraging, Marker Aggregation, and Stop. The results highlight the potential and limitations of Cedrata, providing insights into the future of automatic design processes for robot swarms.AI Generated
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AbstractIn this work, we develop a set of behavioral and conditional modules for the use with behavior trees. We present AutoMoDe-Cedrata, an automatic modular design method that automatically assembles and fine-tunes these modules into behavior trees that control robot swarms. We testCedrataon three missions and, to gain further insights on its effectiveness, we design control software for the same missions using AutoMoDe-Maple, another automatic design method, and by a group of human designers. Results show that the proposed modules allow for well-performing behavior trees. Yet,Cedratahad difficulties automatically generating control software that performs similarly well as the one generated by human designers, especially when involving communication. -
Salp Swarm Optimization Search Based Feature Selection for Enhanced Phishing Websites Detection
Ruba Abu Khurma, Khair Eddin Sabri, Pedro A. Castillo, Ibrahim AljarahThe chapter delves into the challenges of high dimensionality in data pre-processing, particularly in the context of phishing website detection. It introduces the Salp Swarm Optimization algorithm as a novel approach for feature selection, highlighting its advantages in mitigating the exponential time complexity of traditional methods. The authors propose the use of novel transfer functions to enhance the algorithm's performance in the binary space, leading to improved detection accuracy and feature reduction. The study compares various transfer functions and demonstrates the superiority of the proposed method through extensive experiments and results. The chapter concludes with a discussion on future work, emphasizing the potential of applying these techniques to other optimization problems in different domains.AI Generated
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AbstractInternet-connected devices are increasing rapidly. This facilitates transferring most of the real-world transactions to the cyber world. It follows that eCrime is growing continuously. Phishing is a cyber-attack carried out by intruders. They aim to deceive the users of the Internet to achieve their malicious goals. Therefore, experts have developed different approaches to protect financial transactions and personal login information of the users. Their primary concern is to detect the security breaches for online use of the Internet channels (e.g. emails, SMS, webpages, and social platforms). In this paper, we propose a new phishing detection system based on the Salp Swarm Algorithm (SSA). The main objective is to maximize the classification performance and minimize the number of features of the phishing system. Different transfer function (TF) families: S-TFs, V-TFs, X-TFs, U-TFs, and Z-TFs are used to convert the continuous SSA into binary. Sixteen different binary versions of the SSA algorithm are produced based on different TFs. A comparison analysis is performed to pick up the best binarization method. The phishing system is validated by comparing it with three state-of-the-art algorithms. The results show that BSSA with X-TFs achieved the best results in terms of the used evaluation measures. -
Real Time Optimisation of Traffic Signals to Prioritise Public Transport
Milan Wittpohl, Per-Arno Plötz, Neil UrquhartThis chapter delves into the critical role of traffic signals in urban infrastructure and the challenges faced by traffic engineers in optimising traffic efficiency. It introduces the concept of real-time optimisation using evolutionary algorithms and microscopic traffic simulation to prioritise public transportation. The author discusses the limitations of current approaches and presents a novel algorithm, EA-FC, which integrates SUMO simulation into an evolutionary algorithm. The research aims to address the flow prediction problem and real-world traffic regulations, offering a promising solution for improving urban mobility in densely populated cities. The chapter concludes with a summary of the experiments conducted and potential future work, highlighting the significant improvements achieved in traffic efficiency and public transportation prioritisation.AI Generated
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AbstractThis paper examines the optimisation of traffic signals to prioritise public transportation (busses) in real time. A novel representation for the traffic signal prioritisation problem is introduced. The novel representation is used within an evolutionary algorithm that supports safe solutions which comply with real-world traffic signal constraints. The proposed system finds near-optimal solutions in around 20 s, enabling real-time optimisation. The authors examine a specific junction in Hamburg, Germany, based on real-world traffic data a variety of different problem scenarios ranging from low to exceptional traffic saturations are generated. In collaboration with domain experts, a fitness function is defined to reduce the journey time of a bus while maintaining an overall stable traffic system. Candidate solutions are evaluated using the microscopic traffic simulator SUMO allowing for precise optimisation and addressing of the flow prediction problem. The results show good scaling of the proposed system, with more significant improvements in more congested scenarios. Given the results, future research on bigger and multiple road junctions is motivated.This work contributes to the field in four ways. Firstly, by defining a real-world problem containing the actual intersection layout and traffic signal parameters. Secondly, by presenting a software design that integrates highly efficient SUMO simulations into an evolutionary algorithm. Thirdly, by introducing a novel representation that allows unconventional solutions while ensuring compliance with traffic signal regulations at all times. Lastly, by testing the suggested approach on various problem scenarios of the real-world problem. -
Adaptive Covariance Pattern Search
Ferrante NeriThis chapter introduces Adaptive Covariance Pattern Search (ACPS), an advanced algorithm that addresses the limitations of Covariance Pattern Search (CPS). ACPS adapts search directions based on successful points, eliminating the need for a pre-set threshold and continuously refining its search strategy. The algorithm demonstrates superior performance in both unimodal and simple multimodal optimization problems, making it a competitive alternative to prevalent algorithms like CMAES. The chapter provides a detailed explanation of ACPS, its implementation, and numerical results that highlight its advantages over CPS and other methods.AI Generated
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AbstractPattern search is a family of single solution deterministic optimisation algorithms for numerical optimisation. Pattern search algorithms generate a new candidate solution by means of an archive of potential moves, named pattern. This pattern is generated by a basis of vectors that span the domain where the function to optimise is defined.The present article proposes an adaptive implementation of pattern search that performs, at run-time, a fitness landscape analysis of the problem to determine the pattern and adapt it to the geometry of the problem. The proposed algorithm, called Adaptive Covariance Pattern Search (ACPS) uses at the beginning the fundamental orthonormal basis (directions of the variables) to build the pattern. Subsequently, ACPS saves the successful visited solutions, calculates the covariance matrix associated with these samples, and then uses the eigenvectors of this covariance matrix to build the pattern. ACPS is a restarting algorithm that at each restart recalculates the pattern that progressively adapts to the problem to optimise. Numerical results show that the proposed ACPS appears to be a promising approach on various problems and dimensions. -
Evaluating the Success-History Based Adaptive Differential Evolution in the Protein Structure Prediction Problem
Pedro Henrique Narloch, Márcio DornThe chapter delves into the complex Protein Structure Prediction (PSP) problem, highlighting the use of bio-inspired algorithms, particularly the Success-History Based Adaptive Differential Evolution (SHADE). It discusses the challenges of PSP, the application of SHADE, and its comparison with other algorithms like canonical Differential Evolution and Self-Adaptive Differential Evolution (SaDE). The study uses the Angle Probability List (APL) for initial population enhancement, showcasing SHADE's superior performance in energy minimization and structural prediction. The chapter concludes by emphasizing SHADE's potential as a promising metaheuristic for PSP and suggests future research directions.AI Generated
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AbstractProteins are vital macro-molecules for every living organism. As the exper imental determination of protein structures is costly and time-consuming, computational methods became an interesting way to predict proteins’ shape based on their amino acid sequence. Metaheuristics have been employed in the protein structure prediction problem through the years, with different characteristics and different knowledge sources. However, these methods are heavily dependent on parameter tuning, where wrong parameters might cause poor performance. Recently, adaptive strategies were proposed to deal with parameter tuning’s non-trivial task, leaving the algorithm to choose its parameters for each optimization step. Although adaptive metaheuristics are widely applied to benchmark problems, only a few were tested in the PSP problem. To contribute to the analysis of adaptive metaheuristics in the PSP problem, we explore in this work the capability of one of theCEC’14winners: the Success-History based Adaptive Differential Evolution algorithm on the tertiary protein structure prediction problem. We tested the SHADE algorithm in eight different proteins and compared the algorithm to the other two classical non-adaptive differential evolution and the well-known self-adaptive differential evolution. Moreover, we enhanced the SHADE with domain knowledge from APL. Our results enlarge the research body in adaptive methods for the PSP problem, showing that SHADE is better than non-adaptive differential evolution approaches and competitive compared to self-adaptive differential evolution and related works. -
Beyond Body Shape and Brain: Evolving the Sensory Apparatus of Voxel-Based Soft Robots
Andrea Ferigo, Giovanni Iacca, Eric MedvetThis chapter delves into the advanced design of voxel-based soft robots, focusing on the evolutionary optimization of their sensory apparatus. By using evolutionary algorithms, the authors demonstrate how the placement and types of sensors can be optimized to enhance the robots' performance in tasks such as locomotion. The study compares the effectiveness of evolved sensory configurations with manually designed ones, showing that evolution can lead to more efficient and robust designs. The chapter also discusses the impact of sensor strength and body shape on the effectiveness of sensory apparatus, providing insights into the complex interplay between actuation and sensing. The experimental results show that evolution can discover optimal sensor configurations, even under constraints, leading to simpler and more energy-efficient robots. The chapter concludes by suggesting future research directions, including the use of diversity-driven evolutionary algorithms and the co-evolution of body and controller in soft robots.AI Generated
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AbstractBiological and artificial embodied agents behave by acquiring information through sensors, processing that information, and acting on the environment. The sensory apparatus, i.e., the location on the body of the sensors and the kind of information the sensors are able to capture, has a great impact on the agent ability of exhibiting complex behaviors. While in nature, the sensory apparatus is the result of a long-lasting evolution, in artificial agents (robots) it is usually the result of a design choice. However, when the agents are complex and the design space is large, making that choice can be hard. In this paper, we explore the possibility of evolving the sensory apparatus of voxel-based soft robots (VSRs), a kind of simulated robots composed of multiple deformable components. VSRs, due to their intrinsic modularity, allow for great freedom in how to shape the robot body, brain, and sensory apparatus. We consider a set of sensors that allow the agent to sense itself and the environment (using vision and touch) and we show, experimentally, that the effectiveness of the sensory apparatus depends on the shape of the body and on the actuation capability, i.e., the VSR strength. Then we show that evolutionary optimizaemedvet@units.ittion is able to evolve an effective sensory apparatus, even when constraints on the availability of the sensors are posed. By extending the adaptation to the sensory apparatus, beyond the body shape and the brain, we believe that our study takes a step forward to the ambitious path towards self-building robots. -
Desirable Objective Ranges in Preference-Based Evolutionary Multiobjective Optimization
Sandra González-Gallardo, Rubén Saborido, Ana B. Ruiz, Mariano LuqueThe chapter delves into the complexities of multiobjective optimization problems (MOPs), highlighting the importance of finding Pareto optimal solutions. It introduces the concept of preference-based evolutionary multiobjective optimization, where decision-maker preferences are incorporated into the algorithm. The main focus is on the new algorithm, EROF, which approximates the region of interest (ROI) defined by desirable objective function ranges. EROF updates weight vectors using theoretical results, enhancing convergence towards the desired ROI. The chapter also includes a detailed computational experiment that demonstrates the superior performance of EROF compared to other algorithms, especially as the number of objective functions increases. This makes the chapter a valuable resource for researchers and practitioners in the field of optimization.AI Generated
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AbstractIn this paper, we propose a preference-based Evolutionary Multiobjective Optimization algorithm, at which the preferences are given in the form of desirable ranges for the objective functions, i.e. by means of aspiration and reservation levels. The aspiration levels are values to be achieved by the objectives, while the reservation levels are objective values not to be worsen. In the algorithm proposed, the first generations are performed using a set of weight vectors to initially converge to the region of the Pareto optimal front associated with the point formed with the reservation levels. At a certain moment, these weights are updated using the nondominated solutions generated so far, to re-direct the search towards the region which contains the Pareto optimal solutions with objective values among the desirable ranges. To this aim, the remaining number of generations are run using the updated weight vectors and the point formed with the aspiration levels. The computational experiment show the potential of our proposal in 2, 3 and 5-objective problems, in comparison to other state-of-the-art algorithms. -
Improving Search Efficiency and Diversity of Solutions in Multiobjective Binary Optimization by Using Metaheuristics Plus Integer Linear Programming
Miguel Ángel Domínguez-Ríos, Francisco Chicano, Enrique AlbaThe chapter introduces a novel hybrid algorithm, MOFeLS, that enhances search efficiency and diversity in multiobjective binary optimization problems. By integrating integer linear programming (ILP) solvers with metaheuristics, MOFeLS efficiently finds feasible solutions and optimizes them using local search. The method is particularly effective in handling equality constraints, which are challenging for traditional metaheuristics. Extensive computational experiments show that MOFeLS outperforms well-known evolutionary algorithms in terms of solution quality, diversity, and computational efficiency. The chapter also discusses the potential for future research in extending this approach to higher-order objective and constraint functions.AI Generated
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AbstractMetaheuristics for solving multiobjective problems can provide an approximation of the Pareto front in a short time, but can also have difficulties finding feasible solutions in constrained problems. Integer linear programming solvers, on the other hand, are good at finding feasible solutions, but they can require some time to find and guarantee the efficient solutions of the problem. In this work we combine these two ideas to propose a hybrid algorithm mixing an exploration heuristic for multiobjective optimization with integer linear programming to solve multiobjective problems with binary variables and linear constraints. The algorithm has been designed to provide an approximation of the Pareto front that is well-spread throughout the objective space. In order to check the performance, we compare it with three popular metaheuristics using two benchmarks of multiobjective binary constrained problems. The results show that the proposed approach provides better performance than the baseline algorithms in terms of number of the solutions, hypervolume, generational distance, inverted generational distance, and the additive epsilon indicator. -
Automated, Explainable Rule Extraction from MAP-Elites Archives
Neil Urquhart, Silke Höhl, Emma HartThe chapter delves into the challenge of summarizing large datasets generated by MAP-Elites algorithms in the urban logistics domain. It introduces two methods, Genetic Programming and CN2, for extracting policies that describe relationships between problem characteristics and decision variables. The study evaluates the accuracy and complexity of the policies produced by these methods, highlighting the strengths and limitations of each approach. Additionally, it includes a qualitative evaluation by a domain expert, providing valuable insights into how well the generated policies align with user beliefs and expectations. This comprehensive analysis offers a unique perspective on the potential of automated policy extraction to enhance user trust and decision-making in complex, high-dimensional problem spaces.AI Generated
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AbstractQuality-diversity (QD) algorithms that return a large archive of elite solutions to a problem provide insights into how high-performing solutions are distributed throughout a feature-space defined by a user. They are often described as illuminating the feature-space, providing a qualitative illustration of relationships between features and objective quality. However, if there are 1000 s of solutions in an archive, extracting a succinct set of rules that capture these relationships in a quantitative manner (i.e. as a set of rules) is challenging. We propose two methods for the automated generation of rules from data contained in an archive; the first uses Genetic Programming and the second, a rule-induction method known as CN2. Rules are generated from large archives of data produced by running MAP-Elites on an urban logistics problem. A quantitative and qualitative evaluation that includes the end-user demonstrate that the rules are capable of fitting the data, but also highlights some mismatches between the model used by the optimiser and that assumed by the user.
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- Title
- Applications of Evolutionary Computation
- Editors
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Pedro A. Castillo
Juan Luis Jiménez Laredo
- Copyright Year
- 2021
- Publisher
- Springer International Publishing
- Electronic ISBN
- 978-3-030-72699-7
- Print ISBN
- 978-3-030-72698-0
- DOI
- https://doi.org/10.1007/978-3-030-72699-7
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