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14
A Tutorial for Competent Memetic Algorithms: Model, Taxonomy, and Design Issues
- IEEE Transactions on Evolutionary Computation
, 2005
"... We recommend you cite the published version. ..."
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Systematic Integration of Parameterized Local Search into Evolutionary Algorithms
- IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
, 2004
"... Application-specific, parameterized local search algorithms (PLSAs), in which optimization accuracy can be traded off with run time, arise naturally in many optimization contexts. We introduce a novel approach, called simulated heating, for systematically integrating parameterized local search into ..."
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Cited by 18 (1 self)
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Application-specific, parameterized local search algorithms (PLSAs), in which optimization accuracy can be traded off with run time, arise naturally in many optimization contexts. We introduce a novel approach, called simulated heating, for systematically integrating parameterized local search into evolutionary algorithms (EAs). Using the framework of simulated heating, we investigate both static and dynamic strategies for systematically managing the tradeoff between PLSA accuracy and optimization effort. Our goal is to achieve maximum solution quality within a fixed optimization time budget. We show that the simulated heating technique better utilizes the given optimization time resources than standard hybrid methods that employ fixed parameters, and that the technique is less sensitive to these parameter settings. We apply this framework to three different optimization problems, compare our results to the standard hybrid methods, and show quantitatively that careful management of this tradeoff is necessary to achieve the full potential of an EA/PLSA combination.
Hybrid Evolutionary Algorithms: Methodologies, Architectures, and Reviews
, 2007
"... Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, self-adaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even t ..."
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Cited by 8 (1 self)
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Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, self-adaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications in engineering, business, commerce, etc., yet in practice sometimes they deliver only marginal performance. Inappropriate selection of various parameters, representation, etc. are frequently blamed. There is little reason to expect that one can find a uniformly best algorithm for solving all optimization problems. This is in accordance with the No Free Lunch theorem, which explains that for any algorithm, any elevated performance over one class of problems is exactly paid for in performance over another class. Evolutionary algorithm behavior is determined by the exploitation and exploration relationship kept throughout the run. All these clearly illustrates the need for hybrid evolutionary approaches where the main task is to optimize the performance of the direct evolutionary approach. Recently, hybridization of evolutionary algorithms is getting popular due to their capabilities in handling several real world problems involving complexity, noisy environment, imprecision, uncertainty, and vagueness. In this chapter, first we emphasize the need for hybrid evolutionary algorithms and then we illustrate the various possibilities for hybridization of an evolutionary algorithm and also present some of the generic hybrid evolutionary architectures that has evolved during the last couple of decades. We also provide a review of some of the interesting hybrid frameworks reported in the literature.
A Generational Scheme for Partitioning Graphs
, 2001
"... Graph partitioning divides a graph into several pieces by cutting edges. Very effective heuristic partitioning algorithms have been developed which run in real-time, but it is unknown how good the partitions are since the problem is, in general, NP-complete. This paper reports an evolutionary ..."
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Cited by 2 (1 self)
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Graph partitioning divides a graph into several pieces by cutting edges. Very effective heuristic partitioning algorithms have been developed which run in real-time, but it is unknown how good the partitions are since the problem is, in general, NP-complete. This paper reports an evolutionary search algorithm for finding benchmark partitions. Distinctive features are the transmission and modification of whole subdomains (the partitioned units) that act as genes, and the use of a multilevel heuristic algorithm to effect the crossover and mutations. Its effectiveness is demonstrated by improvements on previously established benchmarks.
A Hybrid Genetic Algorithm with Pattern Search for Finding Heavy Atoms in Protein Crystals
"... One approach for determining the molecular structure of proteins is a technique called iso-morphous replacement, in which crystallographers dope protein crystals with heavy atoms, such as mercury or platinum. By comparing measured amplitudes of diffracted x-rays through protein crystals with and wit ..."
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Cited by 2 (0 self)
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One approach for determining the molecular structure of proteins is a technique called iso-morphous replacement, in which crystallographers dope protein crystals with heavy atoms, such as mercury or platinum. By comparing measured amplitudes of diffracted x-rays through protein crystals with and without the heavy atoms, the locations of the heavy atoms can be estimated. Once the locations of the heavy atoms are known, the phases of the diffracted x-rays through the protein crystal can be estimated, which in turn enables the structure of the protein to be estimated. Unfortunately, the key step in this process is the estimation of the locations of the heavy atoms, and this is a multi-modal, non-linear inverse problem. We report results of a pilot study that show that a 2-stage hybrid algorithm, using a stochastic genetic algorithm for stage 1 followed by a deterministic pattern search algorithm for stage 2, can successfully locate up to 5 heavy atoms in computer simulated crystals using noise free data. We conclude that the method may be a viable approach for finding heavy atoms in protein crystals, and suggest ways in which the approach can be scaled up to larger problems.
Memetic Algorithm for Web Service Selection
"... ABSTRACT Due to the changing nature of service-oriented environments, the ability to locate services of interest in such open, dynamic, and distributed environments has become an essential requirement. Current service-oriented architecture standards mainly rely on functional properties, however, se ..."
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ABSTRACT Due to the changing nature of service-oriented environments, the ability to locate services of interest in such open, dynamic, and distributed environments has become an essential requirement. Current service-oriented architecture standards mainly rely on functional properties, however, service registries lack mechanisms for managing services' non-functional properties. Such nonfunctional properties are expressed in terms of quality of service (QoS) attributes. QoS for web services allows consumers to have confidence in the use of services by aiming to experience good service performance in terms of waiting time, reliability, and availability. This paper investigates the service selection process, and proposes two approaches; one that is based on a genetic algorithm, and the other is based on a memetic algorithm to match consumers with services based on QoS attributes as closely as possible. Both approaches are compared with an optimal assignment algorithm called the Munkres algorithm, as well as a Random approach. Measurements are performed to quantify the overall match score, the execution time, and the scalability of all approaches.
Biological Applications Track
"... One approach for determining the molecular structure of proteins is a technique called iso-morphous replacement, in which crystallographers dope protein crystals with heavy atoms, such as mercury or platinum. By comparing measured amplitudes of diffracted x-rays through protein crystals with and wit ..."
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One approach for determining the molecular structure of proteins is a technique called iso-morphous replacement, in which crystallographers dope protein crystals with heavy atoms, such as mercury or platinum. By comparing measured amplitudes of diffracted x-rays through protein crystals with and without the heavy atoms, the locations of the heavy atoms can be estimated. Once the locations of the heavy atoms are known, the phases of the diffracted x-rays through the protein crystal can be estimated, which in turn enables the structure of the protein to be estimated. Unfortunately, the key step in this process is the estimation of the locations of the heavy atoms, and this is a multi-modal, non-linear inverse problem. We report results of a pilot study that show that a 2-stage hybrid algorithm, using a stochastic genetic algorithm for stage 1 followed by a deterministic pattern search algorithm for stage 2, can successfully locate up to 5 heavy atoms in computer simulated crystals using noise free data. We conclude that the method may be a viable approach for finding heavy atoms in protein crystals, and suggest ways in which the approach can be scaled up to larger problems.
COMPARISON OF CROSSOVER OPERATORS FOR THE QUADRATIC ASSIGNMENT PROBLEM
"... Abstract. Crossover (i.e. solution recombination) operators play very important role by constructing competitive genetic algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the quadratic assignment problem (QAP) ..."
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Abstract. Crossover (i.e. solution recombination) operators play very important role by constructing competitive genetic algorithms (GAs). In this paper, the basic conceptual features and specific characteristics of various crossover operators in the context of the quadratic assignment problem (QAP) are discussed. The results of experimental comparison of more than 10 different crossover operators for the QAP are presented. The results obtained demonstrate high efficiency of the crossovers with relatively low degree of disruption, namely, the swap path crossover (SPX), the cohesive crossover (COHX), the one point crossover (OPX). Another promising operator is so-called multiple parent crossover (MPX) operator based on special type of recombination of several solutions-parents. The results from the experiments show that MPX operator enables to achieve better solutions than other operators tested.
A Performance Comparison of GA and ACO Applied to TSP
"... This work presents a contribution to comparing two nature inspired metaheuristics for solving the TSP. We run ACO and GA on three benchmark instances with varying size and complexity, in addition to one real world application in the field of urban transportation and logistics. A first chapter presen ..."
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This work presents a contribution to comparing two nature inspired metaheuristics for solving the TSP. We run ACO and GA on three benchmark instances with varying size and complexity, in addition to one real world application in the field of urban transportation and logistics. A first chapter presents algorithmic approaches. Results and discussion chapter outlines the computational behavior of the algorithms throughout the problem sets. The conclusion closes the discussion with recommendations and future scopes.
