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memetic algorithms and memetic computing optimization: A literature review

SWARM AND EVOLUTIONARY COMPUTATION 2012年 2卷 1-14页

作者： Neri, Ferrante Cotta, Carlos Univ Jyvaskyla Dept Math Informat Technol Jyvaskyla 40014 Finland Univ Malaga Dept Lenguajes & Ciencias Computac Escuela Tecn Super Ingn Informat E-29071 Malaga Spain

memetic computing is a subject in computer science which considers complex structures such as the combination of simple agents and memes, whose evolutionary interactions lead to intelligent complexes capable of problem-solving. The founding cornerstone of this subject has been the concept of memetic algorithms, that is a class of optimization algorithms whose structure is characterized by an evolutionary framework and a list of local search components. This article presents a broad literature review on this subject focused on optimization problems. Several classes of optimization problems, such as discrete, continuous, constrained, multi-objective and characterized by uncertainties, are addressed by indicating the memetic "recipes" proposed in the literature. In addition, this article focuses on implementation aspects and especially the coordination of memes which is the most important and characterizing aspect of a memetic structure. Finally, some considerations about future trends in the subject are given. (C) 2011 Elsevier B.V. All rights reserved.

关键词： memetic computing Evolutionary algorithms memetic algorithms Computational intelligence optimization

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memetic algorithms outperform evolutionary algorithms in multimodal optimisation

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ARTIFICIAL INTELLIGENCE 2020年 287卷 103345-103345页

作者： Phan Trung Hai Nguyen Sudholt, Dirk Univ Birmingham Sch Comp Sci Birmingham B15 2TT W Midlands England Univ Sheffield Dept Comp Sci Sheffield S1 4DP S Yorkshire England

memetic algorithms integrate local search into an evolutionary algorithm to combine the advantages of rapid exploitation and global optimisation. We provide a rigorous runtime analysis of memetic algorithms on the Hurdle problem, a landscape class of tunable difficulty with a "big valley structure", a characteristic feature of many hard combinatorial optimisation problems. A parameter called hurdle width describes the length of fitness valleys that need to be overcome. We show that the expected runtime of plain evolutionary algorithms like the (1+1) EA increases steeply with the hurdle width, yielding superpolynomial times to find the optimum, whereas a simple memetic algorithm, (1+1) MA, only needs polynomial expected time. Surprisingly, while increasing the hurdle width makes the problem harder for evolutionary algorithms, it becomes easier for memetic algorithms. We further give the first rigorous proof that crossover can decrease the expected runtime in memetic algorithms. A (2+1) MA using mutation, crossover and local search outperforms any other combination of these operators. Our results demonstrate the power of memetic algorithms for problems with big valley structures and the benefits of hybridising multiple search operators. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Search heuristics Hybridisation Evolutionary algorithms Black-box optimisation memetic algorithms Time complexity

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memetic algorithms for Multiple Interference Cancellations of Linear Array Based on Phase-Amplitude Perturbations

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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2010年第3期144卷 629-642页

作者： Hsu, C. H. Shyr, W. J. Kuo, K. H. Chou, P. H. Wu, M. J. Chien Kuo Technol Univ Dept Elect Engn Changhua 500 Taiwan Natl Changhua Univ Educ Dept Ind Educ & Technol Changhua 500 Taiwan Natl Changhua Univ Educ Dept Mechatron Engn Changhua 500 Taiwan

A novel method based on the memetic algorithm for the design of multiple interference cancellations of a linear array antenna by phase-amplitude perturbations is proposed. The adaptive array antenna is capable of sensing the presence of interference sources and suppressing the interferences in the interfering directions. This technique can increase the signal-to-interference ratio. The memetic algorithm is applied to find the weighting vector which makes the pattern nulling optimization of the proposed adaptive antenna. This technique is also able to do the cancellation of multiple interferences for different incident directions.

关键词： memetic algorithms Phase and amplitude perturbations Multiple interferences cancellations Linear arrays

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memetic algorithms for Continuous Optimisation Based on Local Search Chains

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EVOLUTIONARY COMPUTATION 2010年第1期18卷 27-63页

作者： Molina, Daniel Lozano, Manuel Garcia-Martinez, Carlos Herrera, Francisco Univ Cadiz Dept Comp Engn Cadiz 11003 Spain Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Cordoba Dept Comp & Numer Anal E-14071 Cordoba Spain

memetic algorithins with continuous local search methods have, arisen as effective tools to address the difficulty of obtaining reliable solutions of high precision for complex continuous optimisation problems. There exists a group of continuous local search algorithms that stand out as exceptional local search optimisers. However, oil some occasions, they may become very expensive, because of the way they exploit local information to guide the search process. In this paper, they are called intensive continuous local search methods. Given the potential of this type of local optimisation methods, it is interesting to build prospective memetic algorithm models with them. This paper presents the concept of local search chain as a springboard to design memetic algorithm approaches that call effectively sue intense continuous local search methods as local search operators. Local search chain concerns the idea that, at one stage, the local search operator may continue the operation of a previous invocation, from the final configuration (initial solution, strategy parameter values, internal variables, etc.) reached by this one. The proposed memetic algorithm favours the formation of local search chains during the memetic algorithm run with the aim of concentrating local tuning in search regions showing promise. In order to study the performance of the new memetic algorithm model, an instance is implemented with CMA-ES as all intense local search method. The benefits of the proposal in comparison to other kinds of memetic algorithms and evolutionary algorithms proposed in the literature to deal with continuous optimisation problems are experimentally shown. Concretely, the empirical study reveals a clear superiority when tackling high-dimensional problems.

关键词： Continuous optimisation memetic algorithms continuous local search algorithms adaptive local search intensity genetic algorithms

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memetic algorithms for the unconstrained binary quadratic programming problem

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BIOSYSTEMS 2004年第1-3期78卷 99-118页

作者： Merz, P Katayama, K Okayama Univ Sci Dept Informat & Comp Engn Okayama 7000005 Japan Univ Kaiserslautern Dept Comp Sci D-67653 Kaiserslautern Germany

This paper presents a memetic algorithm, a highly effective evolutionary algorithm incorporating local search for solving the unconstrained binary quadratic programming problem (BQP). To justify the approach, a fitness landscape analysis is conducted experimentally for several instances of the BQP. The results of the analysis show that recombination-based variation operators are well suited for the evolutionary algorithms with local search. Therefore, the proposed approach includes - besides a highly effective randomized k-opt local search - a new variation operator that has been tailored specially for the application in the hybrid evolutionary framework. The operator is called innovative variation and is fundamentally different from traditional crossover operators, since new genetic material is included in the offspring which is not contained in one of the parents. The evolutionary heuristic is tested on 35 publicly available BQP instances, and it is shown experimentally that the algorithm is capable of finding best-known solutions to large BQPs in a short time and with a high frequency. In comparison to other approaches for the BQP, the approach appears to be much more effective, particularly for large instances of 1000 or 2500 binary variables. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

关键词： memetic algorithms unconstrained binary quadratic programming problem fitness landscape analysis evolutionary algorithm local search combinatorial optimization

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memetic algorithms for training feedforward neural networks: an approach based on gravitational search algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2561-2588页

作者： Garcia-Rodenas, Ricardo Jimenez Linares, Luis Alberto Lopez-Gomez, Julio Univ Castilla La Mancha Dept Math Ciudad Real Spain Univ Castilla La Mancha Dept Informat Technol & Syst Ciudad Real Spain

The backpropagation (BP) algorithm is a gradient-based algorithm used for training a feedforward neural network (FNN). Despite the fact that BP is still used today when FNNs are trained, it has some disadvantages, including the following: (i) it fails when non-differentiable functions are addressed, (ii) it can become trapped in local minima, and (iii) it has slow convergence. In order to solve some of these problems, metaheuristic algorithms have been used to train FNN. Although they have good exploration skills, they are not as good as gradient-based algorithms at exploitation tasks. The main contribution of this article lies in its application of novel memetic approaches based on the Gravitational Search Algorithm (GSA) and Chaotic Gravitational Search Algorithm (CGSA) algorithms, called respectively memetic Gravitational Search Algorithm (MGSA) and memetic Chaotic Gravitational Search Algorithm (MCGSA), to train FNNs in three classical benchmark problems: the XOR problem, the approximation of a continuous function, and classification tasks. The results show that both approaches constitute suitable alternatives for training FNNs, even improving on the performance of other state-of-the-art metaheuristic algorithms such as ParticleSwarm Optimization (PSO), the Genetic Algorithm (GA), the Adaptive Differential Evolution algorithm with Repaired crossover rate (Rcr-JADE), and the Covariance matrix learning and Bimodal distribution parameter setting Differential Evolution (COBIDE) algorithm. Swarm optimization, the genetic algorithm, the adaptive differential evolution algorithm with repaired crossover rate, and the covariance matrix learning and bimodal distribution parameter setting differential evolution algorithm.

关键词： Feedforward neural networks memetic algorithms Gravitational search algorithm Quasi-Newton methods

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memetic algorithms: The polynomial local search complexity theory perspective

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Journal of Mathematical Modelling and algorithms 2008年第1期7卷 3-24页

作者： Krasnogor, Natalio Smith, Jim Automated Scheduling Optimisation and Planning Research Group School of Computer Science and IT University of Nottingham Nottingham United Kingdom Faculty of Computing Engineering and Mathematical Sciences University of the West of England Bristol United Kingdom

In previous work (Krasnogor, http://***/~nxk/*** . In: Studies on the Theory and Design Space of memetic algorithms. Ph.D. thesis, University of the West of England, Bristol, UK, 2002;Krasnogor and Smith, IEEE Trans Evol algorithms 9(6):474-488, 2005) we develop a syntax-only classification of evolutionary algorithms, in particular so-called memetic algorithms (MAs). When "syntactic sugar" is added to our model, we are able to investigate the polynomial local search (PLS) complexity of memetic algorithms. In this paper we show the PLS-completeness of whole classes of problems that occur when memetic algorithms are applied to the travelling salesman problem using a range of mutation, crossover and local search operators. Our PLS-completeness results shed light on the worst case behaviour that can be expected of a memetic algorithm under these circumstances. Moreover, we point out in this paper that memetic algorithms for graph partitioning and maximum network flow (both with important practical applications) also give rise to PLS-complete problems. © 2007 Springer Science + Business Media B.V.

关键词： Evolutionary hybrid algorithms Genetic local search hybrids Graph partitioning Hyper-heuristics memetic algorithms Meta-Lamarckian algorithms Multimeme algorithms Polynomial local search Travelling salesman problem

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memetic algorithms Beat Evolutionary algorithms on the Class of Hurdle Problems 18

Memetic Algorithms Beat Evolutionary Algorithms on the Class...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Phan Trung Hai Nguyen Sudholt, Dirk Univ Birmingham Sch Comp Sci Birmingham W Midlands England Univ Sheffield Dept Comp Sci Sheffield S Yorkshire England

ISBN: (纸本)9781450356183

memetic algorithms are popular hybrid search heuristics that integrate local search into the search process of an evolutionary algorithm in order to combine the advantages of rapid exploitation and global optimisation. However, these algorithms are not well understood and the field is lacking a solid theoretical foundation that explains when and why memetic algorithms are effective. We provide a rigorous runtime analysis of a simple memetic algorithm, the (1+1) MA, on the Hurdle problem class, a landscape class of tuneable difficulty that shows a "big valley structure", a characteristic feature of many hard problems from combinatorial optimisation. The only parameter of this class is the hurdle width w, which describes the length of fitness valleys that have to be overcome. We show that the (1+1) EA requires Theta(n(w)) expected function evaluations to find the optimum, whereas the (1+1) MA with best-improvement and first-improvement local search can find the optimum in Theta(n(2) + n(3) /w(2)) and Theta(n(3) /w(2)) function evaluations, respectively. Surprisingly, while increasing the hurdle width makes the problem harder for evolutionary algorithms, the problem becomes easier for memetic algorithms. We discuss howthese findings can explain and illustrate the success of memetic algorithms for problems with big valley structures.

关键词： Evolutionary algorithms hybridisation iterated local search local search memetic algorithms running time analysis theory

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memetic algorithms for Constructing Binary Covering Arrays of Strength Three

Memetic Algorithms for Constructing Binary Covering Arrays o...

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9th International Conference on Evolution Artificial

作者： Rodriguez-Tello, Eduardo Torres-Jimenez, Jose CINVESTAV Tamaulipas Informat Technol Lab Victoria Tamps 87276 Mexico

ISBN: (纸本)9783642141553

This paper presents a new memetic Algorithm (MA) designed to compute near-optimal solutions for the covering array construction problem. It incorporates several distinguished features including an efficient heuristic to generate a good quality initial population, and a local search operator based on a fine tuned Simulated Annealing (SA) algorithm employing a carefully designed compound neighborhood. Its performance is investigated through extensive experimentation over well known benchmarks and compared with other state-of-the-art algorithms, showing improvements on some previous best-known results.

关键词： memetic algorithms Covering Arrays Software Testing

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Sequential and multi-population memetic algorithms for assigning cells to switches in mobile networks

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COMPUTER NETWORKS 2003年第3期43卷 247-261页

作者： Quintero, A Pierre, S Ecole Polytech Dept Comp Engn Mobile Comp & Networking Res Lab LARIM Montreal PQ H3C 3A7 Canada

The design of mobile networks is a complex process, which requires solving simultaneously many difficult combinatorial optimization problems. Assigning cells to switches in cellular mobile networks is an NP-hard problem, which can not be practically solved using exact methods. In this context, heuristic approaches like memetic algorithms (MAs) are recommended. This paper proposes different MAs to solve this problem. The implementation of these algorithms has been subject to extensive tests. The results obtained confirm the efficiency and the effectiveness of MA to provide good solutions for moderate- and large-sized cellular mobile networks, in comparison with standard genetic algorithm and with other heuristic methods well known in the literature. (C) 2003 Elsevier B.V. All rights reserved.

关键词： cellular networks cell assignment memetic algorithms genetic algorithms tabu search simulated annealing migration multi-population algorithm

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