Coevolving Memetic Algorithms: A Review and Progress Report

Smith, James E.

doi:10.1109/tsmcb.2006.883273

Cited by 150 publications

(83 citation statements)

References 27 publications

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“…Work is already in progress in this area. Looking beyond, another topic for further research is the extension of this analysis to coevolutionary memetic algorithms (Smith, 2007;2012) in which memes are detached from genotypes and co-evolve alongside the latter in a separate population. Spain, in 1994 and1998, respectively.…”

Section: Discussionmentioning

confidence: 99%

A study on meme propagation in multimemetic algorithms

Nogueras

Cotta

2015

International Journal of Applied Mathematics and Computer Science

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Multimemetic algorithms (MMAs) are a subclass of memetic algorithms in which memes are explicitly attached to genotypes and evolve alongside them. We analyze the propagation of memes in MMAs with a spatial structure. For this purpose we propose an idealized selecto-Lamarckian model that only features selection and local improvement, and study under which conditions good, high-potential memes can proliferate. We compare population models with panmictic and toroidal grid topologies. We show that the increased takeover time induced by the latter is essential for improving the chances for good memes to express themselves in the population by improving their hosts, hence enhancing their survival rates. Experiments realized with an actual MMA on three different complex pseudo-Boolean functions are consistent with these findings, indicating that memes are more successful in a spatially structured MMA, rather than in a panmictic MMA, and that the performance of the former is significantly better than that of its panmictic counterpart.

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Section: Discussionmentioning

confidence: 99%

A study on meme propagation in multimemetic algorithms

Nogueras

Cotta

2015

International Journal of Applied Mathematics and Computer Science

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“…Another approach lies in the population based nature of MAs so we have multiple candidate solutions, which makes the task of deciding which LS operator to apply to any given one more complex. [7] So in proposed algorithm, we consider two type candidates to apply local search as a meme. One is local search on the overall best position per cell (gbest) and another, for each pbest a local search is considered.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Memetic-CLA-PSO: A Hybrid Model for Optimization

Akhtari

Meybodi

2011

2011 UkSim 13th International Conference on Computer Modelling and Simulation

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“…In order to increase the converging speed, a new memetic algorithm, namely coevolving memetic algorithm, is developed. Its fundamentals as well as a comprehensive review of the basic approaches based on this algorithm are presented by Smith [56].…”

Section: Hybrid and Parallel Search-based Evolutionary Algorithmmentioning

confidence: 99%

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

Talaslioglu

2009

Modelling and Simulation in Engineering

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A new genetic algorithm (GA) methodology, Bipopulation-Based Genetic Algorithm with Enhanced Interval Search (BGAwEIS), is introduced and used to optimize the design of truss structures with various complexities. The results of BGAwEIS are compared with those obtained by the sequential genetic algorithm (SGA) utilizing a single population, a multipopulation-based genetic algorithm (MPGA) proposed for this study and other existing approaches presented in literature. This study has two goals: outlining BGAwEIS's fundamentals and evaluating the performances of BGAwEIS and MPGA. Consequently, it is demonstrated that MPGA shows a better performance than SGA taking advantage of multiple populations, but BGAwEIS explores promising solution regions more efficiently than MPGA by exploiting the feasible solutions. The performance of BGAwEIS is confirmed by better quality degree of its optimal designations compared to algorithms proposed here and described in literature.

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Coevolving Memetic Algorithms: A Review and Progress Report

Cited by 150 publications

References 27 publications

A study on meme propagation in multimemetic algorithms

A study on meme propagation in multimemetic algorithms

Memetic-CLA-PSO: A Hybrid Model for Optimization

A New Genetic Algorithm Methodology for Design Optimization of Truss Structures: Bipopulation-Based Genetic Algorithm with Enhanced Interval Search

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