A learning automata-based memetic algorithm

Mirsaleh, Mehdi Rezapoor; Meybodi, Mohammad Reza

doi:10.1007/s10710-015-9241-9

Cited by 10 publications

(1 citation statement)

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“…GALA performs according to the Lamarckian learning model because it modifies the genotype and only uses a chromosome's fitness to fitness function computation. Modified GALA (MGALA), which is an extension of GALA, performs according to the Baldwinian learning model . Unlike GALA, which only uses the value of genes for fitness computation, MGALA uses all the information in the OMA representation of the chromosome (ie, the degree of association between genes and their alleles as well as the value of genes) to compute the fitness function.…”

Section: Related Workmentioning

confidence: 99%

Balancing exploration and exploitation in memetic algorithms: A learning automata approach

Mirsaleh

Meybodi

2017

Computational Intelligence

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One of the problems with traditional genetic algorithms (GAs) is premature convergence, which makes them incapable of finding good solutions to the problem. The memetic algorithm (MA) is an extension of the GA. It uses a local search method to either accelerate the discovery of good solutions, for which evolution alone would take too long to discover, or reach solutions that would otherwise be unreachable by evolution or a local search method alone. In this paper, we introduce a new algorithm based on learning automata (LAs) and an MA, and we refer to it as LA‐MA. This algorithm is composed of 2 parts: a genetic section and a memetic section. Evolution is performed in the genetic section, and local search is performed in the memetic section. The basic idea of LA‐MA is to use LAs during the process of searching for solutions in order to create a balance between exploration performed by evolution and exploitation performed by local search. For this purpose, we present a criterion for the estimation of success of the local search at each generation. This criterion is used to calculate the probability of applying the local search to each chromosome. We show that in practice, the proposed probabilistic measure can be estimated reliably. On the basis of the relationship between the genetic section and the memetic section, 3 versions of LA‐MA are introduced. LLA‐MA behaves according to the Lamarckian learning model, BLA‐MA behaves according to the Baldwinian learning model, and HLA‐MA behaves according to both the Baldwinian and Lamarckian learning models. To evaluate the efficiency of these algorithms, they have been used to solve the graph isomorphism problem. The results of computer experimentations have shown that all the proposed algorithms outperform the existing algorithms in terms of quality of solution and rate of convergence.

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Section: Related Workmentioning

confidence: 99%