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Finding low autocorrelation binary sequences with memetic algorithms

APPLIED SOFT COMPUTING 2009年第4期9卷 1252-1262页

作者： Gallardo, Jose E. Cotta, Carlos Fernandez, Antonio J. Univ Malaga Dept Lenguajes & Ciencias Computac E-29071 Malaga Spain

This paper deals with the construction of binary sequences with low autocorrelation, a very hard problem with many practical applications. The paper analyzes several metaheuristic approaches to tackle this kind of sequences. More specifically, the paper provides an analysis of different local search strategies, used as stand-alone techniques and embedded within memetic algorithms. One of our proposals, namely a memetic algorithm endowed with a Tabu Search local searcher, performs at the state-of-the-art, as it consistently finds optimal sequences in considerably less time than previous approaches reported in the literature. Moreover, this algorithm is also able to provide new best-known solutions for large instances of the problem. In addition, a variant of this algorithm that explores only a promising subset of the whole search space (known as skew-symmetric sequences) is also analyzed. Experimental results show that this new algorithm provides new best-known solutions for very large instances of the problem. (C) 2009 Elsevier B. V. All rights reserved.

关键词： Low autocorrelation binary sequences memetic algorithms Tabu Search Combinatorial optimization

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Optimizing two-level reverse distribution networks with hybrid memetic algorithms

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OPTIMIZATION LETTERS 2014年第2期8卷 753-762页

作者： Freitas, A. R. R. Silva, V. M. R. Campelo, F. Guimares, F. G. Univ Fed Minas Gerais Grad Program Elect Engn BR-31270901 Belo Horizonte MG Brazil Univ Fed Fluminense Inst Comp BR-24210240 Niteroi RJ Brazil Univ Fed Minas Gerais Dept Engn Eletr BR-31270010 Belo Horizonte MG Brazil

In a Two-Level Reverse Distribution Network, products are returned from customers to manufacturers through collection and refurbishing sites. The costs of the reverse chain often overtake the costs of the forward chain by many times. With some known algorithms for the problem as reference, we propose a hybrid memetic algorithm that uses linear programming and a heuristic for defining routes. Moreover, we describe heuristics for deciding locations, algorithms to define routes for the products, and problem-specific genetic operators. memetic algorithms have returned the best results for all instances.

关键词： Evolutionary computation memetic algorithms Reverse distribution networks Logistics

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Real-coded memetic algorithms with crossover hill-climbing

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EVOLUTIONARY COMPUTATION 2004年第3期12卷 273-302页

作者： Lozano, M Herrera, F Krasnogor, N Molina, D Univ Granada Dept Comp Sci & AI E-18071 Granada Spain Univ Nottingham Sci Comp Sci & IT Automat Scheduling Optimisat & Planning Grp Nottingham NG8 1BB England

This paper presents a real-coded memetic algorithm that applies a crossover hill-climbing to solutions produced by the genetic operators. On the one hand, the memetic algorithm provides global search (reliability) by means of the promotion of high levels of population diversity. On the other, the crossover hill-climbing exploits the self-adaptive capacity of real-parameter crossover operators with the aim of producing an effective local tuning on the solutions (accuracy). An important aspect of the memetic algorithm proposed is that it adaptively assigns different local search probabilities to individuals. It was observed that the algorithm adjusts the global/local search balance according to the particularities of each problem instance. Experimental results show that, for a wide range of problems, the method we propose here consistently outperforms other real-coded memetic algorithms which appeared in the literature.

关键词： memetic algorithms real-coding steady-stated genetic algorithms crossover hill-climbing

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Self-adaptive memetic algorithms for multi-objective single machine learning-effect scheduling problems with release times

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2022年第3期34卷 748-784页

作者： Deliktas, Derya Kutahya Dumlupynar Univ Fac Engn Dept Ind Engn Kutahya Turkey Univ Nottingham Sch Comp Sci Computat Optimisat & Learning COL Lab Nottingham NG8 1BB England

This paper proposes a single machine scheduling problem with learning-effect and release times by considering two objectives requiring minimization of makespan and total tardiness, simultaneously. Due to the NP-hardness of this problem, two memetic algorithms with meme variants are presented for solving the bi-objective problem and applied by combining three different scalarization methods, including weighted sum, conic, and tchebycheff. The performance of all memetic algorithms with the meme is investigated across randomly generated twenty-seven test problems ranging from 'small' to 'large' size. The experimental results indicate that the Multimeme memetic Algorithm using the tchebycheff outperforms the other algorithms producing the best-known results for almost all bi-objective single machine scheduling instances with learning-effects. All algorithms perform effectively in solving large-sized problems with up to 200 jobs.

关键词： memetic algorithms Hyper-heuristics Single machine scheduling Scalarization methods Learning effect

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Multiobjective memetic algorithms with quadratic approximation-based local search for expensive optimization in electromagnetics

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IEEE TRANSACTIONS ON MAGNETICS 2008年第6期44卷 1126-1129页

作者： Wanner, Elizabeth F. Guimaraes, Frederico G. Takahashi, Ricardo H. C. Lowther, David A. Ramirez, Jaime A. Univ Fed Minas Gerais Dept Elect Engn BR-31270010 Belo Horizonte MG Brazil Univ Fed Ouro Preto Dept Matemat BR-35400000 Ouro Preto MG Brazil Univ Fed Minas Gerais Dept Math BR-31270010 Belo Horizonte MG Brazil McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2K6 Canada

We describe a local search procedure for multiobjective genetic algorithms that employs quadratic approximations for all nonlinear functions involved in the optimization problem. The samples obtained by the algorithm during the evolutionary process are used to fit these quadratic approximations in the neighborhood of the point selected for local search, implying that no extra cost of function evaluations is required. After that, a locally improved solution is easily estimated from the associated quadratic problem. We demonstrate the hybridization of our procedure with the well-known multiobjective genetic algorithm. This methodology can also be coupled with other multiobjective evolutionary algorithms. The results show that the proposed procedure is suitable for time-demanding black-box optimization problems.

关键词： approximation techniques hybrid algorithms memetic algorithms

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Local search with quadratic approximations into memetic algorithms for optimization with multiple criteria

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EVOLUTIONARY COMPUTATION 2008年第2期16卷 185-224页

作者： Wanner, Elizabeth F. Guimaraes, Frederico G. Takahashi, Ricardo H. C. Fleming, Peter J. Univ Fed Ouro Preto Dept Matemat BR-35400000 Ouro Preto MG Brazil Univ Sheffield Dept Automat Control & Syst Engn Sheffield S1 3JD S Yorkshire England Univ Fed Minas Gerais Dept Engn Eletr BR-31270010 Belo Horizonte MG Brazil Univ Fed Minas Gerais Dept Matemat BR-31270010 Belo Horizonte MG Brazil

This paper proposes a local search optimizer that, employed as an additional operator in multiobjective evolutionary techniques, can help to find more precise estimates of the Pareto-optimal surface with a smaller cost of function evaluation. The new operator employs quadratic approximations of the objective functions and constraints, which are built using only the function samples already produced by the usual evolutionary algorithm function evaluations. The local search phase consists of solving the auxiliary multiobjective quadratic optimization problem defined from the quadratic approximations, scalarized via a goal attainment formulation using an LMI solver. As the determination of the new approximated solutions is performed without the need of any additional function evaluation, the proposed methodology is suitable for costly black-box optimization problems.

关键词： nonlinear programming multiobjective optimization memetic algorithms genetic algorithms function approximation linear matrix inequalities costly black-box function

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Automated design of prestressed concrete precast road bridges with hybrid memetic algorithms

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REVISTA INTERNACIONAL DE METODOS NUMERICOS PARA CALCULO Y DISENO EN INGENIERIA 2014年第3期30卷 145-154页

作者： Marti, J. V. Yepes, V. Gonzalez-Vidosa, F. Luz, A. Univ Politecn Valencia Inst Ciencia & Tecnol Hormigon ICITECH Valencia 46022 Spain Univ Politecn Valencia Dept Ingn Construcc Valencia 46022 Spain

This paper deals with the minimum cost automatic design of precast bridge decks made of U-beams and an upper slab. It uses a hybrid memetic algorithm that combines the population search of solutions by genetic algorithms and a search by variable neighborhood. This algorithm is applied to a bridge made of two isostatic U-beams of 20-40 m of span and a width of 12 m. This example has 40 discrete variables. The evaluation module takes into account the service and ultimate limit states usually considered for these structures, i.e. flexure, shear, torsion, cracking, deflections, etc. The use of the memetic algorithm requires its previous calibration. Each of the heuristics is run 12 times, obtaining information about the minimum and average values, as well as the scatter. The parametric study showed a good correlation for the cost, the number of strands and the steel and concrete quantities with the span length. Savings have been found between 8 and 50% compared to other structures really executed. The presented procedure allows the practical application to the real design and its adaptation to the precast process. (C) 2012 CIMNE (Universitat Politecnica de Catalunya). Published by Elsevier Espana, S.L. All rights reserved.

关键词： Concrete structures Heuristic optimization Precast beams Prestressed concrete structures memetic algorithms Structural design Bridges

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Parallel machine scheduling problems using memetic algorithms

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COMPUTERS & INDUSTRIAL ENGINEERING 1997年第3-4期33卷 761-764页

作者： Cheng, RW Gen, M Ashikaga Inst Technol Dept Ind & Syst Engn Ashikaga 326 Japan

In this paper, we investigate how to apply the hybrid genetic algorithms (the memetic algorithms) to solve the parallel machine scheduling problem. There are two essential issues to be dealt with for all kinds of parallel machine scheduling problems: job partition among machines and job sequence within each machine. The basic idea of the proposed method is that (a) use the genetic algorithms to evolve the job partition and then (b) apply a local optimizer to adjust the job permutation to push each chromosome climb to his local optima. Preliminary computational experiments demonstrate that the hybrid genetic algorithm outperforms the genetic algorithms and the conventional heuristics. (C) 1997 Elsevier Science Ltd.

关键词： parallel machine scheduling hybrid genetic algorithms memetic algorithms

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Fitness Landscapes, memetic algorithms, and Greedy Operators for Graph Bipartitioning

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EVOLUTIONARY COMPUTATION 2000年第1期8卷 61-91页

作者： Merz, Peter Freisleben, Bernd Univ Siegen Dept Comp Sci D-57068 Siegen Germany

The fitness landscape of the graph bipartitioning problem is investigated by performing a search space analysis for several types of graphs. The analysis shows that the structure of the search space is significantly different for the types of instances studied. Moreover, with increasing epistasis, the amount of gene interactions in the representation of a solution in an evolutionary algorithm, the number of local minima for one type of instance decreases and, thus, the search becomes easier. We suggest that other characteristics besides high epistasis might have greater influence on the hardness of a problem. To understand these characteristics, the notion of a dependency graph describing gene interactions is introduced. In particular, the local structure and the regularity of the dependency graph seems to be important for the performance of an algorithm, and in fact, algorithms that exploit these properties perform significantly better than others which do not. It will be shown that a simple hybrid multi-start local search exploiting locality in the structure of the graphs is able to find optimum or near optimum solutions very quickly. However, if the problem size increases or the graphs become unstructured, a memetic algorithm (a genetic algorithm incorporating local search) is shown to be much more effective.

关键词： Combinatorial optimization graph bipartitioning fitness landscapes search space analysis memetic algorithms genetic local search

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A Novel Approach for Automated Music Composition Using memetic algorithms 11

A Novel Approach for Automated Music Composition Using Memet...

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49th Annual Association-for-Computing-Machinery (ACM) Southeast Conference (ACMSE)

作者： Wells, Derek ElAarag, Hala Stetson Univ Dept Math & Comp Sci 421 N Woodland Blvd Deland FL 32828 USA

ISBN: (纸本)9781450306867

Music has been an integral part of society for hundreds of years. Since the advent and rise of computers and computer technologies, musicians have utilized these advances in their craft, whether it is in electronic music or the notation of musical scores. The field of memetic computing is a new and developing one. A few researchers are using memetic algorithms to solve real world problems. This research offers a novel approach to produce quality musical compositions using a memetic algorithm. Unlike other computer music composition proposals in the literature, our proposal does not need any intervention from the user. Our preliminary results are very promising and conforms with MIDI protocol standards;the industry standard for electronic musical instruments.

关键词： memetic Computing memetic algorithms music composition

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