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Memetic Computing

Memetic Computing OnlineFirst articles

20.09.2019 | Regular Research Paper

Estimation of distribution evolution memetic algorithm for the unrelated parallel-machine green scheduling problem

With the increasing concern on greenhouse gas emissions, green scheduling decision in the manufacturing factory is gaining more and more attention. This paper addresses the unrelated parallel machine green scheduling problem (UPMGSP) with criteria …

12.09.2019 | Regular Research Paper

Deep memetic models for combinatorial optimization problems: application to the tool switching problem

Memetic algorithms are techniques that orchestrate the interplay between population-based and trajectory-based algorithmic components. In particular, some memetic models can be regarded under this broad interpretation as a group of autonomous …

08.08.2019 | Regular Research Paper

An Adaptive Island Evolutionary Algorithm for the berth scheduling problem

Increasing volumes of the seaborne containerized trade put additional pressure on marine container terminal operators. Long congestion periods have been reported at certain marine container terminals due to inability of the infrastructure to serve …

16.07.2019 | Regular Research Paper

Multi-objective flow shop scheduling with limited buffers using hybrid self-adaptive differential evolution

In this paper, a self-adaptive differential evolution (DE) algorithm is designed to solve multi-objective flow shop scheduling problems with limited buffers (FSSPwLB). The makespan and the largest job delay are treated as two separate objectives …

01.07.2019 | Regular Research Paper

A memetic algorithm with optimal recombination for the asymmetric travelling salesman problem

We propose a new memetic algorithm with optimal recombination for the asymmetric travelling salesman problem (ATSP). The optimal recombination problem (ORP) is solved in a crossover operator based on a new exact algorithm that solves the ATSP on …

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Memetic Computing's goals are:

  • To be an outlet for high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains. We seek to dissolve the barriers separating metaheuristics, exact and approximation algorithms research and to bring forth a renewed impetus towards the investigation and understanding of promising new hybrid algorithmic technologies.
  • To go beyond current search methodologies towards innovative research on the emergence of cultural artifacts such as game, trade and negotiation strategies and, more generally, rules of behavior as they apply to, for example, robotic, multi-agent and artificial life systems.
  • Ultimately, Memetic Computing aspires to serve as a focal publication where the latest results in Natural Computation, Artificial Intelligence, Machine Learning, Operational Research and Natural Sciences (e.g. cognitive, animal and insect's behavior, etc.) are fuzzed together in novel ways in order to transcend the intrinsic limitations of a single discipline.
  • Potential authors are invited to submit original research articles for publication consideration. Reviews and short communications may also be considered.

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