Memetic algorithms and memetic computing optimization: A literature review

Neri, Ferrante; Cotta, Carlos

doi:10.1016/j.swevo.2011.11.003

Cited by 553 publications

(218 citation statements)

References 121 publications

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“…In [22] an interactive algorithm based on R-NSGA-II is proposed, and in [23] R-NSGA-II is modified by integrating a stochastic local search in a memetic fashion, see [47], [10], [32], and [72].…”

Section: Progressive Preference Articulation: a Brief Reviewmentioning

confidence: 99%

Progressive preference articulation for decision making in multi-objective optimisation problems

Rostami

Neri

Epitropakis

2017

ICA

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Abstract.This paper proposes a novel algorithm for addressing multi-objective optimisation problems, by employing a progressive preference articulation approach to decision making. This enables the interactive incorporation of problem knowledge and decision maker preferences during the optimisation process. A novel progressive preference articulation mechanism, derived from a statistical technique, is herein proposed and implemented within a multi-objective framework based on evolution strategy search and hypervolume indicator selection. The proposed algorithm is named the Weighted Z-score Covariance Matrix Adaptation Pareto Archived Evolution Strategy with Hypervolume-sorted Adaptive Grid Algorithm (WZ-HAGA).WZ-HAGA is based on a framework that makes use of evolution strategy logic with covariance matrix adaptation to perturb the solutions, and a hypervolume indicator driven algorithm to select successful solutions for the subsequent generation. In order to guide the search towards interesting regions, a preference articulation procedure composed of four phases and based on the weighted z-score approach is employed. The latter procedure cascades into the hypervolume driven algorithm to perform the selection of the solutions at each generation.Numerical results against five modern algorithms representing the state-of-the-art in multi-objective optimisation demonstrate that the proposed WZ-HAGA outperforms its competitors in terms of both the hypervolume indicator and pertinence to the regions of interest.

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Section: Progressive Preference Articulation: a Brief Reviewmentioning

confidence: 99%

Progressive preference articulation for decision making in multi-objective optimisation problems

Rostami

Neri

Epitropakis

2017

ICA

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“…MAs are used in order to tackle MOPs [53]. The benefit of MAs in EAs is that they use local search for the presented problem [54].…”

Section: Memetic Techniquementioning

confidence: 99%

“…Moscato considered MA as a type of population-based integrated GA with individual learning. MAs are now used in several areas, including hybrid evolutionary algorithms.MAs are used in order to tackle MOPs [53]. The benefit of MAs in EAs is that they use local search for the presented problem [54].…”

mentioning

confidence: 99%

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

Saleh¹,

Shamsuddin²

2015

Int J Swarm Intel Evol Comput

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Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many realworld optimization problems include several contradictory objectives. Rather than single optimization, Multi-Objective Optimization (MOO) can be utilized as a set of optimal solutions to solve these problems. In this paper, Harmony Search (HS) and memetic approach was used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental results have proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure. Unlike previous study mentioned earlier in Jin [14] and other single objective studies [18,19], this paper deals with an improved multi objective method to obtain simple and accurate ESNN. The proposed method evolves toward optimal values defined by several objectives with model accuracy and ESNN's structure to improve performance for classification problems. The remaining parts of this paper are organized as follows: Methods including: Evolving Spiking Neural Network, Multi Objective Differential Evolution are presented in section 2. In addition, section 3 clarifies the proposed method Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) used in this paper, Experimental design is discussed in section 4, section 5 explains in detail the results and discussion, and finally, section 6 concludes the paper with future works. MethodsThis section reviews the vital foundation of evolving spiking neural network (ESNN) and discusses the evolutionary algorithms that have been utilized for enhancement. In the first part, an introduction of ESNN, neuron coding, learning method and ESNN design, and its algorithm is presented. The second part focuses on the algorithms which are used for improvement of ESNN. The concepts and methods of multi objective optimization (MOO) are highlighted. After that, the literature focuses on the working of differential evolution, harmony search (HS) algorithm and memetic approach which is used to improve and enhance the performance of classification . Evolving spiking neural network (ESNN)Currently, several enhancements of SNN have been proposed. Wysoski improved one of these new models known as Evolving Spiking Neural Network (ESNN) [20]. Generally, ESNN used the principles of evolving connectionist systems (ECOS) where neurons are created incrementall...

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“…Memetic algorithms, introduced by Moscato [12], hybridise genetic algorithms with local search. Recent developments in memetic computing, which broadens the concept of memes, can be found in [13]. Both the algorithm components and parameter values need to be specified in advance [17], however determining the appropriate components and initial parameter settings (i.e., parameter tuning) to obtain high quality solutions can take a large computational time.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Taguchi Method for Tuning a Memetic Algorithm with Reduced Computational Time Budget

Gumus

Özcan

Atkin

2016

Communications in Computer and Information Science

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Abstract. Determining the best initial parameter values for an algorithm, called parameter tuning, is crucial to obtaining better algorithm performance; however, it is often a time-consuming task and needs to be performed under a restricted computational budget. In this study, the results from our previous work on using the Taguchi method to tune the parameters of a memetic algorithm for cross-domain search are further analysed and extended. Although the Taguchi method reduces the time spent finding a good parameter value combination by running a smaller size of experiments on the training instances from different domains as opposed to evaluating all combinations, the time budget is still larger than desired. This work investigates the degree to which it is possible to predict the same good parameter setting faster by using a reduced time budget. The results in this paper show that it was possible to predict good combinations of parameter settings with a much reduced time budget. The good final parameter values are predicted for three of the parameters, while for the fourth parameter there is no clear best value, so one of three similarly performing values is identified at each time instant.

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Memetic algorithms and memetic computing optimization: A literature review

Cited by 553 publications

References 121 publications

Progressive preference articulation for decision making in multi-objective optimisation problems

Progressive preference articulation for decision making in multi-objective optimisation problems

Memetic Harmony Search Algorithm Based on Multi-objective Differ-ential Evolution of Evolving Spiking Neural Networks

An Analysis of the Taguchi Method for Tuning a Memetic Algorithm with Reduced Computational Time Budget

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