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Harnessing memetic algorithms: a practical guide

TOP 2025年 1-30页

作者： Cotta, Carlos Univ Malaga ETSI Informat Dept Lenguajes & Ciencias Comp Campus TeatinosOff 3-2-49 E-29071 Malaga Spain Univ Malaga ITIS Software Ada Byron Res BldgC Arquitecto Francisco Penalosa Malaga 29071 Spain

The aim of this work is to provide a didactic approximation to memetic algorithms (MAs) and how to apply these techniques to an optimization problem. MAs are based on the synergistic combination of ideas from population-based metaheuristics and trajectory-based search/optimization techniques. Most commonly, MAs feature a population-based algorithm as the underlying search engine, endowing it with problem-specific components for exploring the search space, and in particular with local-search mechanisms. In this work, we describe the design of the different elements of the MA to fit the problem under consideration, and go on to perform a detailed case study on a constrained combinatorial optimization problem related to aircraft landing scheduling. An outline of some advanced topics and research directions is also provided.

关键词： memetic algorithms Evolutionary computation Local search GRASP Aircraft landing scheduling

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Future trends in the design of memetic algorithms: the case of the linear ordering problem

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Neural Computing and Applications 2025年 1-15页

作者： Pérez Lugo, Lázaro Jesús Segura, Carlos Miranda, Gara Guanajuato Mexico Departamento de Ingeniería Informática y de Sistemas Universidad de La Laguna Camino San Francisco de Paula 19 Canarias Santa Cruz de Tenerife Spain

The way heuristic optimizers are designed has evolved over the decades, as computing power has increased. Such has been the case for the linear ordering problem (LOP), a field in which trajectory-based strategies led the way during the 1990 s, but which have now been surpassed by memetic schemes. This paper focuses on understanding how the design of LOP optimizers will change in the future, as computing power continues to increase, yielding two main contributions. On the one hand, a metaheuristic was designed that is capable of effectively exploiting a large amount of computational resources, specifically computing power equivalent to what a recent core can output during runs lasting over four months. Our analyses show that as the power of the computational resources increases, it will be necessary to boost the capacities of the intensification methods applied in the memetic algorithms to keep the population from stagnating. And, on the other hand, the best-known results for today’s most challenging set of instances (xLOLIB2) were significantly outperformed. New bounds were established in this benchmark, which provides a new reference frame for future research. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Design of optimizers High-performance capabilities Linear ordering problem memetic algorithms

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memetic algorithms based on local search chains for large scale continuous optimisation problems: MA-SSW-Chains

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SOFT COMPUTING 2011年第11期15卷 2201-2220页

作者： Molina, Daniel Lozano, Manuel Sanchez, Ana M. Herrera, Francisco Univ Cadiz Dept Comp Languages & Syst Cadiz Spain Univ Granada Dept Comp Sci & Artificial Inteligence Granada Spain Univ Granada Dept Software Engn Granada Spain

Nowadays, large scale optimisation problems arise as a very interesting field of research, because they appear in many real-world problems (bio-computing, data mining, etc.). Thus, scalability becomes an essential requirement for modern optimisation algorithms. In a previous work, we presented memetic algorithms based on local search chains. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, starting from the final configuration reached by this one. Using this technique, it was presented a memetic algorithm, MA-CMA-Chains, using the CMA-ES algorithm as its local search component. This proposal obtained very good results for continuous optimisation problems, in particular with medium-size (with up to dimension 50). Unfortunately, CMA-ES scalability is restricted by several costly operations, thus MA-CMA-Chains could not be successfully applied to large scale problems. In this article we study the scalability of memetic algorithms based on local search chains, creating memetic algorithms with different local search methods and comparing them, considering both the error values and the processing cost. We also propose a variation of Solis Wets method, that we call Subgrouping Solis Wets algorithm. This local search method explores, at each step of the algorithm, only a random subset of the variables. This subset changes after a certain number of evaluations. Finally, we propose a new memetic algorithm based on local search chains for high dimensionality, MA-SSW-Chains, using the Subgrouping Solis Wets' algorithm as its local search method. This algorithm is compared with MA-CMA-Chains and different reference algorithms, and it is shown that the proposal is fairly scalable and it is statistically very competitive for high-dimensional problems.

关键词： memetic algorithms Continuous optimisation Large scale problems Local search chains

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memetic algorithms applied to the optimization of workflow compositions

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SWARM AND EVOLUTIONARY COMPUTATION 2013年 10卷 31-40页

作者： Ludwig, Simone A. N Dakota State Univ Dept Comp Sci Fargo ND 58105 USA

The selection of services of a workflow based on Quality of Service (QoS) attributes is an important issue in service-oriented systems. QoS attributes allow for a better selection process based on nonfunctional quality criteria such as reliability, availability, and response time. Past research has mostly addressed this problem with optimal methods such as linear programming approaches. Given the nature of service-oriented systems where large numbers of services are available with different QoS values, optimal methods are not suitable and therefore, approximate techniques are necessary. In this paper, we investigate Genetic algorithms and particle swarm optimization for the service selection process. In particular, both methods are combined with an optimal assignment algorithm (Munkres algorithm) in order to achieve higher solution qualities (success ratios) and to form a so called memetic algorithm. Experiments are conducted to investigate the suitability of the approaches and to compare the memetic algorithms with their non-memetic counterparts. The results reveal that the memetic algorithms are very suitable for the application to the workflow selection problem. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Genetic algorithms Particle swarm optimization Munkres algorithm Quality of service memetic algorithms

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memetic algorithms-based artificial multiplicative neural models selection for resolving multi-component mixtures based on dynamic responses

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2007年第2期85卷 232-242页

作者： Hervas, Cesar Silva, Manuel Univ Cordoba Dept Analyt Chem E-14071 Cordoba Spain Univ Cordoba Dept Comp Sci E-14071 Cordoba Spain

The potential of the product unit neural networks built by using different memetic evolutionary algorithms for the simultaneous determination of mixtures of analytes based on dynamic responses was investigated. For this purpose, three methodologies for obtaining the structure and weights of neural networks were proposed, based on the combination of the evolutionary programming algorithm, a clustering process and a local improvement procedure carried out by the Levenberg-Marquardt algorithm. To test these approaches, two phenol derivatives, pyrogallol and gallic acid, were quantified in mixtures based on their perturbation effect in a classical oscillating chemical system, namely, the Belousov-Zhabotinskyi reaction. The four-parameter Weibull curve associated with the profile of perturbation response estimated by the Levenberg-Marquardt method was used as input data for the models. Straightforward network topologies 4:3:1 (13 weights) and 4:2:1 (9 weights) for pyrogallol and gallic acid, respectively, allowed the analytes to be quantified with great accuracy (mean standard error of prediction for the generalization test) and precision (standard deviation) of 2.45% and 0.21 for pyrogallol and 7.61% and 1.63 for gallic acid. The selected model can be easily implemented via software by using simple quantification equations, from which significant chemical remarks can be inferred. Finally, the product unit neural network modelling offered better results when compared with sigmoidal neural networks and response surface analysis. (c) 2006 Elsevier B.V All rights reserved.

关键词： memetic algorithms product unit neural networks multi-component mixtures pyrogallol gallic acid oscillating reactions

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memetic algorithms for parallel code optimization

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INTERNATIONAL JOURNAL OF PARALLEL PROGRAMMING 2007年第1期35卷 33-61页

作者： Oezcan, Ender Onbasioglu, Esin Yeditepe Univ TR-34755 Istanbul Turkey

Discovering the optimum number of processors and the distribution of data on distributed memory parallel computers for a given algorithm is a demanding task. A memetic algorithm (MA) is proposed here to find the best number of processors and the best data distribution method to be used for each stage of a parallel program. Steady state memetic algorithm is compared with transgenerational memetic algorithm using different crossover operators and hill-climbing methods. A self-adaptive MA is also implemented, based on a multimeme strategy. All the experiments are carried out on computationally intensive, communication intensive, and mixed problem instances. The MA performs successfully for the illustrative problem instances.

关键词： distributed memory parallel computers memetic algorithms parallelizing compilers search methods

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memetic algorithms for De Novo Motif Discovery

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IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2012年第5期16卷 730-748页

作者： Chan, Tak-Ming Leung, Kwong-Sak Lee, Kin-Hong Chinese Univ Hong Kong Dept Comp Sci & Engn Shatin Hong Kong Peoples R China

Identifying the unknown transcription factor binding sites (TFBSs) is a fundamental and important component for understanding gene regulation as well as life mechanisms. The corresponding de novo motif discovery problem in bioinformatics is formulated as pattern discovery from strings, where challenges come from both modeling and optimization, because the short TFBSs are weak signals in massive and noisy experimental data. While genetic algorithms have been widely applied to the problem, recent memetic algorithms (MAs) employing local operators demonstrate the superiority in both effectiveness and efficiency. In this paper, we propose and study various MA components including local operators and models for motif discovery, through the newly established MA framework. The demonstrated optimization and modeling capabilities are analyzed in-depth on real datasets and their noisy versions. Selected optimal MAs show significantly improved performance over state-of-the-art methods in extensive tests including the blind test on the eukaryotic benchmark. This paper serves as the first systematic study of MAs on de novo motif discovery, where important issues are highlighted in the analyses of MA design. The comprehensive component categorization and the MA framework provide a useful platform for future MA developments, especially on the newly emerging chromatin immunoprecipitation followed by sequencing data.

关键词： Bioinformatics evaluation functions local operators memetic algorithms motif discovery TFBS identification

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memetic algorithms and hyperheuristics applied to a multiobjectivised two-dimensional packing problem

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JOURNAL OF GLOBAL OPTIMIZATION 2014年第4期58卷 769-794页

作者： Segredo, Eduardo Segura, Carlos Leon, Coromoto Univ La Laguna Dpto Estadist IO & Computac San Cristobal la Laguna 38271 Santa Cruz De T Spain

Packing problems are np-hard problems with several practical applications. A variant of a 2d Packing Problem (2dpp) was proposed in the gecco 2008 competition session. In this paper, memetic algorithms (mas) and Hyperheuristics are applied to a multiobjectivised version of the 2dpp. Multiobjectivisation is the reformulation of a mono-objective problem into a multi-objective one. The main aim of multiobjectivising the 2dpp is to avoid stagnation in local optima. First generation mas refers to hybrid algorithms that combine a population-based global search with an individual learning process. A novel first generation ma is proposed, and an original multiobjectivisation method is applied to the 2dpp. In addition, with the aim of facilitating the application of such first generation mas from the point of view of the parameter setting, and of enabling their usage in parallel environments, a parallel hyperheuristic is also applied. Particularly, the method applied here is a hybrid approach which combines a parallel island-based model and a hyperheuristic. The main objective of this work is twofold. Firstly, to analyse the advantages and drawbacks of a set of first generation mas. Secondly, to attempt to avoid those drawbacks by applying a parallel hyperheuristic. Moreover, robustness and scalability analyses of the parallel scheme are included. Finally, we should note that our methods improve on the current best-known solutions for the tested instances of the 2dpp.

关键词： memetic algorithms Hyperheuristics Multiobjectivisation Packing problems Parameter setting

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memetic algorithms for de novo motif-finding in biomedical sequences

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2012年第1期56卷 1-17页

作者： Bi, Chengpeng Childrens Mercy Hosp & Clin Bioinformat & Intelligent Comp Lab Div Clin Pharmacol Kansas City MO 64108 USA Univ Missouri Sch Comp & Engn Sch Med Kansas City MO 64108 USA

Objectives: The objectives of this study are to design and implement a new memetic algorithm for de novo motif discovery, which is then applied to detect important signals hidden in various biomedical molecular sequences. Methods and materials: In this paper, memetic algorithms are developed and tested in de nova motif-finding problems. Several strategies in the algorithm design are employed that are to not only efficiently explore the multiple sequence local alignment space, but also effectively uncover the molecular signals. As a result, there are a number of key features in the implementation of the memetic motif-finding algorithm (MaMotif), including a chromosome replacement operator, a chromosome alteration-aware local search operator, a truncated local search strategy, and a stochastic operation of local search imposed on individual learning. To test the new algorithm, we compare MaMotif with a few of other similar algorithms using simulated and experimental data including genomic DNA, primary microRNA sequences (let-7 family), and transmembrane protein sequences. Results: The new memetic motif-finding algorithm is successfully implemented in C++, and exhaustively tested with various simulated and real biological sequences. In the simulation, it shows that MaMotif is the most time-efficient algorithm compared with others, that is, it runs 2 times faster than the expectation maximization (EM) method and 16 times faster than the genetic algorithm-based EM hybrid. In both simulated and experimental testing, results show that the new algorithm is compared favorably or superior to other algorithms. Notably, MaMotif is able to successfully discover the transcription factors' binding sites in the chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-Seq) data, correctly uncover the RNA splicing signals in gene expression, and precisely find the highly conserved helix motif in the transmembrane protein sequences, as well as rightly detect the p

关键词： Multiple local sequence alignment memetic algorithms Motif discovery Maximum likelihood Biomedical molecular sequences

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memetic algorithms for optimizing adaptive linear array patterns by phase-position perturbations

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2005年第4期24卷 327-341页

作者： Hsu, CH Shyr, WJ Chien Kuo Technol Univ Dept Elect Engn Changhua 500 Taiwan Natl Changhua Univ Educ Dept Ind Educ & Technol Changhua 500 Taiwan

In this paper, based on the phase-position perturbation method, an innovative optimal adaptive antenna technique is proposed, where the deduced radiation pattern formulas available for searching optimal solutions are used to search the optimal weighting vector. The optimal radiation pattern designs of adaptive antenna are studied by the phase-position perturbation method. memetic algorithms are used to search the optimal weighting vector of the phase-position perturbations for the array factor. The design for an optimal radiation pattern of an adaptive antenna can not only adjustably suppress the interferers by placing nulls at the directions of the interfering sources, but at the same time provide a maximum main lobe in the direction of the desired signal, i.e., to maximize the signal-to-interference ratio. To achieve this goal, a new convergent method, referred to as the two-way convergent method for memetic algorithms, is proposed. The memetic algorithm combines a genetic algorithm and local search heuristics to solve combinatorial optimization problems. The memetic algorithm is a kind of improved type of the traditional genetic algorithm. By using a local search procedure, it can avoid the shortcomings of the traditional genetic algorithm, whose termination criteria are set up by using the trial and error method. This proposed method is also able to solve the multipath problem, which exists at the same time in this communication system. The optimal radiation pattern concept can be implemented in practical wireless communication systems. Simulation results are also given in this paper.

关键词： memetic algorithms adaptive linear array phase-position perturbations array factor

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