The Experts below are selected from a list of 6840 Experts worldwide ranked by ideXlab platform
Yong Liang - One of the best experts on this subject based on the ideXlab platform.
-
genetic algorithm with adaptive elitist population strategies for multimodal Function optimization
Applied Soft Computing, 2011Co-Authors: Yong Liang, Kwongsak LeungAbstract:This paper introduces a new technique called adaptive elitist-population search method. This technique allows Unimodal Function optimization methods to be extended to efficiently explore multiple optima of multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and a novel direction dependent elitist genetic operators. Incorporation of the new multimodal technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, we have integrated the new technique into Genetic Algorithms (GAs), yielding an Adaptive Elitist-population based Genetic Algorithm (AEGA). AEGA has been shown to be very efficient and effective in finding multiple solutions of complicated benchmark and real-world multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including rough and stepwise multimodal Functions. Empirical results are also compared with other multimodal evolutionary algorithms from the literature, showing that AEGA generally outperforms existing approaches.
-
adaptive elitist population based genetic algorithm for multimodal Function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive elitist-population search method for allowing Unimodal Function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
-
adaptive elitist population based genetic algorithm for multimodal Function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive elitist-population search method for allowing Unimodal Function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
Kwongsak Leung - One of the best experts on this subject based on the ideXlab platform.
-
genetic algorithm with adaptive elitist population strategies for multimodal Function optimization
Applied Soft Computing, 2011Co-Authors: Yong Liang, Kwongsak LeungAbstract:This paper introduces a new technique called adaptive elitist-population search method. This technique allows Unimodal Function optimization methods to be extended to efficiently explore multiple optima of multimodal problems. It is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and a novel direction dependent elitist genetic operators. Incorporation of the new multimodal technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, we have integrated the new technique into Genetic Algorithms (GAs), yielding an Adaptive Elitist-population based Genetic Algorithm (AEGA). AEGA has been shown to be very efficient and effective in finding multiple solutions of complicated benchmark and real-world multimodal optimization problems. We demonstrate this by applying it to a set of test problems, including rough and stepwise multimodal Functions. Empirical results are also compared with other multimodal evolutionary algorithms from the literature, showing that AEGA generally outperforms existing approaches.
-
adaptive elitist population based genetic algorithm for multimodal Function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive elitist-population search method for allowing Unimodal Function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
-
adaptive elitist population based genetic algorithm for multimodal Function optimization
Genetic and Evolutionary Computation Conference, 2003Co-Authors: Kwongsak Leung, Yong LiangAbstract:This paper introduces a new technique called adaptive elitist-population search method for allowing Unimodal Function optimization methods to be extended to efficiently locate all optima of multimodal problems. The technique is based on the concept of adaptively adjusting the population size according to the individuals' dissimilarity and the novel elitist genetic operators. Incorporation of the technique in any known evolutionary algorithm leads to a multimodal version of the algorithm. As a case study, genetic algorithms(GAs) have been endowed with the multimodal technique, yielding an adaptive elitist-population based genetic algorithm(AEGA). The AEGA has been shown to be very efficient and effective in finding multiple solutions of the benchmark multimodal optimization problems.
Ralph R. Martin - One of the best experts on this subject based on the ideXlab platform.
-
a sequential niche technique for multimodal Function optimization
Evolutionary Computation, 1993Co-Authors: David Beasley, David Bull, Ralph R. MartinAbstract:A technique is described that allows Unimodal Function optimization methods to be extended to locate all optima of multimodal problems efficiently. We describe an algorithm based on a traditional genetic algorithm (GA). This technique involves iterating the GA but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found. This gain is achieved by applying a fitness derating Function to the raw fitness Function, so that fitness values are depressed in the regions of the problem space where solutions have already been found. Consequently, the likelihood of discovering a new solution on each iteration is dramatically increased. The technique may be used with various styles of GAs or with other optimization methods, such as simulated annealing. The effectiveness of the algorithm is demonstrated on a number of multimodal test Functions. The technique is at least as fast as fitness sharing methods. It provides an acceleration of between 1 and l0p on a problem with p optima, depending on the value of p and the convergence time complexity.
C P Katsaras - One of the best experts on this subject based on the ideXlab platform.
-
a saw tooth genetic algorithm combining the effects of variable population size and reinitialization to enhance performance
IEEE Transactions on Evolutionary Computation, 2006Co-Authors: V K Koumousis, C P KatsarasAbstract:A genetic algorithm (GA) is proposed that uses a variable population size and periodic partial reinitialization of the population in the form of a saw-tooth Function. The aim is to enhance the overall performance of the algorithm relying on the dynamics of evolution of the GA and the synergy of the combined effects of population size variation and reinitialization. Preliminary parametric studies to test the validity of these assertions are performed for two categories of problems, a multimodal Function and a Unimodal Function with different features. The proposed scheme is compared with the conventional GA and micro GA (/spl mu/GA) of equal computing cost and guidelines for the selection of effective values of the involved parameters are given, which facilitate the implementation of the algorithm. The proposed algorithm is tested for a variety of benchmark problems and a problem generator from which it becomes evident that the saw-tooth scheme enhances the overall performance of GAs.
David Beasley - One of the best experts on this subject based on the ideXlab platform.
-
a sequential niche technique for multimodal Function optimization
Evolutionary Computation, 1993Co-Authors: David Beasley, David Bull, Ralph R. MartinAbstract:A technique is described that allows Unimodal Function optimization methods to be extended to locate all optima of multimodal problems efficiently. We describe an algorithm based on a traditional genetic algorithm (GA). This technique involves iterating the GA but uses knowledge gained during one iteration to avoid re-searching, on subsequent iterations, regions of problem space where solutions have already been found. This gain is achieved by applying a fitness derating Function to the raw fitness Function, so that fitness values are depressed in the regions of the problem space where solutions have already been found. Consequently, the likelihood of discovering a new solution on each iteration is dramatically increased. The technique may be used with various styles of GAs or with other optimization methods, such as simulated annealing. The effectiveness of the algorithm is demonstrated on a number of multimodal test Functions. The technique is at least as fast as fitness sharing methods. It provides an acceleration of between 1 and l0p on a problem with p optima, depending on the value of p and the convergence time complexity.
