GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms

Peña, José M.; Robles, Víctor; Larrañaga, Pedro; Herves, Vanessa; Rosales, Francisco; Pérez, Marı́a S.

doi:10.1007/978-3-540-24677-0_38

Cited by 51 publications

(30 citation statements)

References 8 publications

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“…This is a point that has already been made, and some proposals for addressing the issue have been laid out [23,24,25,26,27]. This loss of diversity can be traced back to the above outliers issue of model-building algorithms.…”

Section: The Model-building Issuementioning

confidence: 99%

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Martí

García

Berlanga

et al. 2011

Lecture Notes in Computer Science

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Abstract. The introduction of learning to the search mechanisms of optimization algorithms has been nominated as one of the viable approaches when dealing with complex optimization problems, in particular with multi-objective ones. One of the forms of carrying out this hybridization process is by using multi-objective optimization estimation of distribution algorithms (MOEDAs). However, it has been pointed out that current MOEDAs have a intrinsic shortcoming in their model-building algorithms that hamper their performance.In this work we argue that error-based learning, the class of learning most commonly used in MOEDAs is responsible for current MOEDA underachievement. We present adaptive resonance theory (ART) as a suitable learning paradigm alternative and present a novel algorithm called multi-objective ART-based EDA (MARTEDA) that uses a Gaussian ART neural network for model-building and an hypervolume-based selector as described for the HypE algorithm. In order to assert the improvement obtained by combining two cutting-edge approaches to optimization an extensive set of experiments are carried out. These experiments also test the scalability of MARTEDA as the number of objective functions increases.

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Section: The Model-building Issuementioning

confidence: 99%

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Martí

García

Berlanga

et al. 2011

Lecture Notes in Computer Science

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“…We vary the truncation size between 10% and 50% using a step size of 10 and learning rate from 0.1 to 1.0 using a step size of 0.1. Given that EDAs and GAs traverse the search space in different ways, there has been research on combining both algorithms [21], [22]. Apart from generating all mode solutions with EDA, we consider combining crossover (GA) and sampling of the probabilistic model (EDA) for mode generation.…”

Section: 72mentioning

confidence: 99%

BPGA-EDA for the multi-mode resource constrained project scheduling problem

Ayodele

McCall

Regnier-Coudert

2016

2016 IEEE Congress on Evolutionary Computation (CEC)

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“…Some researchers have conducted similar studies in their publications [11][12][13]. Their key idea is to use the EDA as an alternative to the genetic algorithms so as to further enhance the quality of the solution by implementing the searching strategy of intensification using the EDAs and diversification via GAs.…”

Section: Relative Work Of Alternating Edas With Gasmentioning

confidence: 99%

“…Some well-known EDAs include cGA [3], UMDA [4], GA-EDA [11], Guided Mutation (EA/G) [6], Model-Based Evolutionary Algorithm (EA) [12], Artificial Chromosomes with Genetic Algorithms (ACGA) [13], Self-Guided GA [14], and VNS·EDAs [9], etc. For the detailed review, please refer to [7].…”

Section: Introductionmentioning

confidence: 99%

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

Chen

Chang

et al. 2011

Entropy

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An Estimation of Distribution Algorithm (EDA), which depends on explicitly sampling mechanisms based on probabilistic models with information extracted from the parental solutions to generate new solutions, has constituted one of the major research areas in the field of evolutionary computation. The fact that no genetic operators are used in EDAs is a major characteristic differentiating EDAs from other genetic algorithms (GAs). This advantage, however, could lead to premature convergence of EDAs as the probabilistic models are no longer generating diversified solutions. In our previous research [1], we have presented the evidences that EDAs suffer from the drawback of premature convergency, thus several important guidelines are provided for the design of effective EDAs. In this paper, we validated one guideline for incorporating other meta-heuristics into the EDAs. An algorithm named "EA/G-GA" is proposed by selecting a well-known EDA, EA/G, to work with GAs. The proposed algorithm was tested on the NP-Hard single machine scheduling problems with the total weighted earliness/tardiness cost in a just-in-time environment. The experimental results indicated that the EA/G-GA outperforms the compared algorithms statistically significantly across different stopping criteria and demonstrated the robustness of the proposed algorithm. Consequently, this paper is of interest and importance in the field of EDAs.

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GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms

Cited by 51 publications

References 8 publications

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

Multi-Objective Optimization with an Adaptive Resonance Theory-Based Estimation of Distribution Algorithm: A Comparative Study

BPGA-EDA for the multi-mode resource constrained project scheduling problem

EA/G-GA for Single Machine Scheduling Problems with Earliness/Tardiness Costs

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