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Genetic Algorithms and Evolutionary Computation

Genetic Algorithms and Evolutionary Computation
10 Jahrgänge | 2001 - 2004

Beschreibung

Researchers and practitioners alike are increasingly turning to search, optimization, and machine-learning procedures based on natural selection and genetics to solve problems across the spectrum of human endeavor. These genetic algorithms and techniques of evolutionary computation are solving problems and inventing new hardware and software that rival human designs.
Genetic Algorithms and Evolutionary Computation will publish research monographs, edited collections, and graduate-level texts in this rapidly growing field. Primary areas of coverage include the theory, implementation, and application of genetic algorithms (GAs), evolution strategies (ESs), evolutionary programming (EP), learning classifier systems (LCSs) and other variants of genetic and evolutionary computation (GEC). Proposals in related fields such as artificial life, adaptive behavior, artificial immune systems, agent-based systems, neural computing, fuzzy systems, and quantum computing will be considered for publication in this series as long as GEC techniques are part of or inspiration for the system being described. Manuscripts describing GEC applications in all areas of engineering, commerce, the sciences, and the humanities are encouraged.

Alle Bücher der Reihe Genetic Algorithms and Evolutionary Computation

2004 | Buch

Frontiers of Evolutionary Computation

2003 | Buch

Evolutionary Algorithms for Embedded System Design

Evolutionary Algorithms for Embedded System Design describes how Evolutionary Algorithm (EA) concepts can be applied to circuit and system design - an area where time-to-market demands are critical. EAs create an interesting alternative to other …

2002 | Buch

OmeGA

A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems

OmeGA: A Competent Genetic Algorithm for Solving Permutation and Scheduling Problems addresses two increasingly important areas in GA implementation and practice. OmeGA, or the ordering messy genetic algorithm, combines some of the latest in …

2002 | Buch

Anticipatory Learning Classifier Systems

Anticipatory Learning Classifier Systems describes the state of the art of anticipatory learning classifier systems-adaptive rule learning systems that autonomously build anticipatory environmental models. An anticipatory model specifies all …

2002 | Buch

Evolutionary Optimization in Dynamic Environments

Evolutionary Algorithms (EAs) have grown into a mature field of research in optimization, and have proven to be effective and robust problem solvers for a broad range of static real-world optimization problems. Yet, since they are based on the …

2002 | Buch

Noisy Optimization With Evolution Strategies

Noise is a common factor in most real-world optimization problems. Sources of noise can include physical measurement limitations, stochastic simulation models, incomplete sampling of large spaces, and human-computer interaction. Evolutionary …

2002 | Buch

Estimation of Distribution Algorithms

A New Tool for Evolutionary Computation

Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation is devoted to a new paradigm for evolutionary computation, named estimation of distribution algorithms (EDAs). This new class of algorithms generalizes genetic …


2002 | Buch

The Design of Innovation

Lessons from and for Competent Genetic Algorithms

7 69 6 A DESIGN APPROACH TO PROBLEM DIFFICULTY 71 1 Design and Problem Difficulty 71 2 Three Misconceptions 72 3 Hard Problems Exist 76 4 The 3-Way Decomposition and Its Core 77 The Core of Intra-BB Difficulty: Deception 5 77 6 The Core of …

2002 | Buch

Evolutionary Algorithms for Solving Multi-Objective Problems

Researchers and practitioners alike are increasingly turning to search, op­ timization, and machine-learning procedures based on natural selection and natural genetics to solve problems across the spectrum of human endeavor. These genetic …

2001 | Buch

Efficient and Accurate Parallel Genetic Algorithms

As genetic algorithms (GAs) become increasingly popular, they are applied to difficult problems that may require considerable computations. In such cases, parallel implementations of GAs become necessary to reach high-quality solutions in …