Memetic Algorithms

Cotta, Carlos; Mathieson, Luke; Moscato, Pablo

doi:10.1007/978-3-319-07124-4_29

Cited by 17 publications

(12 citation statements)

References 230 publications

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“…Memetic algorithms are flexible metaheuristic approaches (Cotta et al [2018]). This means that they are higher-level procedures, and can be applied to solve a variety of problems.…”

Section: State-of-the-art Of Memetic Algorithmsmentioning

confidence: 99%

Solving the paintshop scheduling problem with memetic algorithms

Weintritt

Musliu

Winter

2021

Proceedings of the Genetic and Evolutionary Computation Conference

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“…Memetic algorithms are flexible metaheuristic approaches (Cotta et al [2018]). This means that they are higher-level procedures, and can be applied to solve a variety of problems.…”

Section: State-of-the-art Of Memetic Algorithmsmentioning

confidence: 99%

Solving the paintshop scheduling problem with memetic algorithms

Weintritt

Musliu

Winter

2021

Proceedings of the Genetic and Evolutionary Computation Conference

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“…MAs, first proposed by Moscato [14,22], represent a recent growing area of research in evolutionary computation. The MA meta-heuristics essentially combine an EA with local search techniques that can be seen as a learning procedure that makes individuals capable of performing local refinements.…”

Section: Memetic Algorithmsmentioning

confidence: 99%

“…Population-based evolutionary approaches, such as genetic algorithms (GAs) or particle swarm optimization (PSO) [12], are able to detect in a fast way the main regions of attraction, while their local search abilities represent a major drawback [13] from the point of view of solution accuracy and of the convergence behaviour, especially when applied to multimodal problems. MAs [1,14] have recently received attention as effective meta-heuristics to improve generic EA schemes by combining these latter with local search (LS) procedures [13,15]. In MAs, EA operations are employed for global rough exploration, and LS operators are used to execute further exploitation of single EA individuals.…”

Section: Introductionmentioning

confidence: 99%

Memes Evolution in a Memetic Variant of Particle Swarm Optimization

et al. 2019

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In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.

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“…For instance, conventional evolutionary algorithms (EAs) can only estimate the optimum search space area within a cost‐effective time and present great problems in fine‐tuning solutions [28–32]. These disadvantages can be overwhelmed by applying exploitative search to optimise the final population of solutions estimated by the EAs [33, 34]. Both effectiveness and solutions' quality are enhanced using this two‐step approach.…”

Section: Introductionmentioning

confidence: 99%

“…Both effectiveness and solutions' quality are enhanced using this two‐step approach. This big family of optimisation methods, which have elements from metaheuristic and EAs, and also include local learning or improvement procedures are generally known as MAs [34]. These have a number of advantages, such as simple implementation and capability to deal with different functional problem representations [33, 34].…”

Section: Introductionmentioning

confidence: 99%