A Modern Introduction to Memetic Algorithms

Moscato, Pablo; Cotta, Carlos

doi:10.1007/978-1-4419-1665-5_6

Cited by 81 publications

(38 citation statements)

References 172 publications

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“…6 we are able to prove the ergodicity of the Markov chain expressing the dynamics of the metaheuristics being in accordance with the EMAS architecture.…”

Section: Introductionmentioning

confidence: 63%

“…Initially, this practical lesson was theoretically backed up initially by [3] and later by [4] in the conspicuous No-Free-Lunch Theorem. The realization of this lesson constitutes one of the raisons-d'être of optimization techniques such as memetic algorithms (MAs) [5][6][7]. These are population-based techniques that blend together ideas from other meta-heuristics, most commonly in terms of integrating local search (LS) within the population-based search engine.…”

Section: Introductionmentioning

confidence: 99%

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Asymptotic guarantee of success for multi-agent memetic systems

Byrski¹,

Schaefer²,

Smółka³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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Abstract. The paper introduces a stochastic model for a class of population-based global optimization meta-heuristics, that generalizes existing models in the following ways. First of all, an individual becomes an active software agent characterized by the constant genotype and the meme that may change during the optimization process. Second, the model embraces the asynchronous processing of agent's actions. Third, we consider a vast variety of possible actions that include the conventional mixing operations (e.g. mutation, cloning, crossover) as well as migrations among demes and local optimization methods. Despite the fact that the model fits many popular algorithms and strategies (e.g. genetic algorithms with tournament selection) it is mainly devoted to study memetic algorithms.The model is composed of two parts: EMAS architecture (data structures and management strategies) allowing to define the space of states and the framework for stochastic agent actions and the stationary Markov chain described in terms of this architecture. The probability transition function has been obtained and the Markov kernels for sample actions have been computed.The obtained theoretical results are helpful for studying metaheuristics conforming to the EMAS architecture. The designed synchronization allows the safe, coarse-grained parallel implementation and its effective, sub-optimal scheduling in a distributed computer environment. The proved strong ergodicity of the finite state Markov chain results in the asymptotic stochastic guarantee of success, which in turn imposes the liveness of a studied metaheuristic. The Markov chain delivers the sampling measure at an arbitrary step of computations, which allows further asymptotic studies, e.g. on various kinds of the stochastic convergence.

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“…6 we are able to prove the ergodicity of the Markov chain expressing the dynamics of the metaheuristics being in accordance with the EMAS architecture.…”

Section: Introductionmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Asymptotic guarantee of success for multi-agent memetic systems

Byrski¹,

Schaefer²,

Smółka³

et al. 2013

Bulletin of the Polish Academy of Sciences: Technical Sciences

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show abstract

“…In order to solve these problems, future work will be devoted to the design of variants of this proposal or more specialised evolutionary approaches. For instance, memetic algorithms (Moscato & Cotta, 2010;Ang et al, 2010), which combine evolutionary operators and local search procedures, would be a good option. Real-coded chromosomes would allow to assign weights to each of the joints.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Evolutionary joint selection to improve human action recognition with RGB-D devices

Chaaraoui

Padilla-López

Climent-Pérez

et al. 2014

Expert Systems with Applications

138

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Interest in RGB-D devices is increasing due to their low price and the wide range of possible applications that come along. These devices provide a marker-less body pose estimation by means of skeletal data consisting of 3D positions of body joints. These can be further used for pose, gesture or action recognition. In this work, an evolutionary algorithm is used to determine the optimal subset of skeleton joints, taking into account the topological structure of the skeleton, in order to improve the final success rate. The proposed method has been validated using a state-of-the-art RGB action recognition approach, and applying it to the MSR-Action3D dataset. Results show that the proposed algorithm is able to significantly improve the initial recognition rate and to yield similar or better success rates than the state-of-the-art methods.

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“…Memetic algorithms [18] denote a broad class of metaheuristics that extend global search methods, such as GA, by incorporating problem-specific knowledge, usually in the form of a local search strategy or through the use of specialised search operators. Thus, a memetic algorithm hybridizes global and local search, such that the individuals of a population in a global search algorithm have the opportunity for local improvement in terms of local search.…”

Section: A Memetic Algorithmsmentioning

confidence: 99%

Solving the Free Clustered TSP Using a Memetic Algorithm

Alsheddy¹

2017

ijacsa

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Abstract-The free clustered travelling salesman problem (FCTSP) is an extension of the classical travelling salesman problem where the set of vertices is partitioned into clusters, and the task is to find a minimum cost Hamiltonian tour such that the vertices in any cluster are visited contiguously. This paper proposes the use of a memetic algorithm (MA) that combines the global search ability of Genetic Algorithm with local search to refine solutions to the FCTSP. The effectiveness of the proposed algorithm is examined on a set of TSPLIB instances with up to 318 vertices and clusters varying between 2 and 50 clusters. Moreover, the performance of the MA is compared with a Genetic Algorithm and a GRASP with path relinking. The computational results confirm the effectiveness of the MA in terms of both solution quality and computational time.

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A Modern Introduction to Memetic Algorithms

Cited by 81 publications

References 172 publications

Asymptotic guarantee of success for multi-agent memetic systems

Asymptotic guarantee of success for multi-agent memetic systems

Evolutionary joint selection to improve human action recognition with RGB-D devices

Solving the Free Clustered TSP Using a Memetic Algorithm

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