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Abstract

In this paper we will introduce the Memetic Algorithms FRAmework, a general purpose evolutionary computation framework. MAFRA allows the construction of complex evolutionary ...
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... For the last few years, a larger number of optimization frameworks have been developed such as: DREAM [29], MALLBA [2], ECJ [36], BEAGEL [6,12], J-DEAL [19], EASY LOCAL + + [15], MAFRA [25], ParadisEO [5]. According to certain criteria these frameworks can support the class of evolutionary algorithms (EA) [36,6,19,29,25,2,5], and/or the class of Single-Solution based Algorithms (SSL) [15,29,25,2,5]. ...
... For the last few years, a larger number of optimization frameworks have been developed such as: DREAM [29], MALLBA [2], ECJ [36], BEAGEL [6,12], J-DEAL [19], EASY LOCAL + + [15], MAFRA [25], ParadisEO [5]. According to certain criteria these frameworks can support the class of evolutionary algorithms (EA) [36,6,19,29,25,2,5], and/or the class of Single-Solution based Algorithms (SSL) [15,29,25,2,5]. Actually, each software tool uses its own strategy to run its various methods, starting with the choice of programming language (most are objectoriented JAVA or C++) then specifying the cooperation and hybridization strategy between the resolution methods (EA/EA) [36,6,19,29,25,2,5], (EA/SSL) [2,5], (SSL/SSL) [15,2,5], and finally, the choice of execution mode: sequential execution [15,25,2,5] and / or parallel execution [36,6,19,29,2,5]. ...
... For the last few years, a larger number of optimization frameworks have been developed such as: DREAM [29], MALLBA [2], ECJ [36], BEAGEL [6,12], J-DEAL [19], EASY LOCAL + + [15], MAFRA [25], ParadisEO [5]. According to certain criteria these frameworks can support the class of evolutionary algorithms (EA) [36,6,19,29,25,2,5], and/or the class of Single-Solution based Algorithms (SSL) [15,29,25,2,5]. Actually, each software tool uses its own strategy to run its various methods, starting with the choice of programming language (most are objectoriented JAVA or C++) then specifying the cooperation and hybridization strategy between the resolution methods (EA/EA) [36,6,19,29,25,2,5], (EA/SSL) [2,5], (SSL/SSL) [15,2,5], and finally, the choice of execution mode: sequential execution [15,25,2,5] and / or parallel execution [36,6,19,29,2,5]. ...
Article
Full-text available
Evolutionary algorithms (EAs) are a range of problem-solving techniques based on mechanisms inspired by biological evolution. Nowadays, EAs have proven their ability and effectiveness to solve combinatorial problems. However, these methods require a considerable time of calculation. To overcome this problem, several parallelization strategies have been proposed in the literature. In this paper, we present a new parallel agent-based EC framework for solving numerical optimization problems in order to optimize computation time and solutions quality.
... Few frameworks available on the Web are devoted to EAs, and their hybridization. MALLBA [2], MAFRA (Java Mimetic Algorithms Framework) [20] and Par-adisEO are good examples of such frameworks. MAFRA is developed in Java using design patterns [16]. ...
Article
In this chapter, a clear difference is made between the parallel design aspect and the parallel implementation aspect of evolutionary algorithms (EAs). From the algorithmic design point of view, the main parallel models for EAs are presented. A unifying view of parallel models for EAs is outlined. This chapter is organized as follows. In Sect. 55.2, the main parallel models for designing EAs are presented. Section 55.3 deals with the implementation issues of parallel EAs. In this section, the main concepts of parallel architectures and parallel programming paradigms, which interfere with the design and implementation of parallel EAs, are outlined. The main performance indicators that can be used to evaluate a parallel EAs in terms of efficiency are detailed. Finally, Sect. 55.4 deals with the design and implementation of different parallel models for EAs based on the software framework ParadisE0.
... Few frameworks available on the Web are devoted to EAs, and their hybridization. MALLBA [2], MAFRA (Java Mimetic Algorithms Framework) [20] and Par-adisEO are good examples of such frameworks. MAFRA is developed in Java using design patterns [16]. ...
Chapter
Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.
Chapter
Metaheuristic algorithms will gain more and more popularity in the future as optimization problems are increasing in size and complexity. In order to record experiences and allow project to be replicated, a standard process as a methodology for designing and implementing metaheuristic algorithms is necessary. To the best of the authors’ knowledge, no methodology has been proposed in literature for this purpose. This paper presents a Design and Implementation Methodology for Metaheuristic Algorithms, named DIMMA. The proposed methodology consists of three main phases and each phase has several steps in which activities that must be carried out are clearly defined in this paper. In addition, design and implementation of tabu search metaheuristic for travelling salesman problem is done as a case study to illustrate applicability of DIMMA.
Conference Paper
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Computational Intelligence techniques represent an emerging computational paradigm because they have been successful in solving complex problems in the most diverse areas, however, despite their usefulness, there are few tools that facilitate handling CI techniques to solve a specific problem and, to the best of our knowledge, there is no paper that offers a complete map of this line of research. Thus, this work presents a Systematic Mapping (SM) aiming at identifying tools that provide support to the development of intelligent systems, summarizing the results obtained, identifying gaps in this research area and indicating points to be explored in future works.
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