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Multi-phase discrete particle swarm optimization
- in FEA 2000: Fourth International Workshop on Frontiers in Evolutionary Algorithms
, 2000
"... This paper describes a successful adaptation of the ..."
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This paper describes a successful adaptation of the
Extending the Scalability of Linkage Learning Genetic Algorithms: Theory and Practice
, 2004
"... There are two primary objectives of this dissertation. The first goal is to identify certain limits of genetic algorithms that use only fitness for learning genetic linkage. Both an ex-planatory theory and experimental results to support the theory are provided. The other goal is to propose a better ..."
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Cited by 10 (2 self)
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There are two primary objectives of this dissertation. The first goal is to identify certain limits of genetic algorithms that use only fitness for learning genetic linkage. Both an ex-planatory theory and experimental results to support the theory are provided. The other goal is to propose a better design of the linkage learning genetic algorithm. After under-standing the cause of the performance barrier, the design of the linkage learning genetic algorithm is modified accordingly to improve its performance on uniformly scaled problems. This dissertation starts with presenting the background of the linkage learning genetic algorithm. Then, it introduces the use of promoters on the chromosome to improve the performance of the linkage learning genetic algorithm on uniformly scaled problems. The convergence time model is constructed by identifying the sequential behavior, developing the tightness time model, and establishing the connection in between. The use of subchro-mosome representations is to avoid the limit implied by the convergence time model. The experimental results demonstrate that the use of subchromosome representations may be a promising way to design a better linkage learning genetic algorithm.
A Survey of Linkage Learning Techniques in Genetic and Evolutionary Algorithms
, 2007
"... This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms ..."
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This paper reviews and summarizes existing linkage learning techniques for genetic and evolutionary algorithms in the literature. It first introduces the definition of linkage in both biological systems and genetic algorithms. Then, it discusses the importance for genetic and evolutionary algorithms to be capable of learning linkage, which is referred to as the relationship between decision variables. Existing linkage learning methods proposed in the literature are reviewed according to different facets of genetic and evolutionary algorithms, including the means to distinguish between good linkage and bad linkage, the methods to express or represent linkage, and the ways to store linkage information. Studies related to these linkage learning methods and techniques are also investigated in this survey.
Designing Genetic Algorithm Based on Exploration and Exploitation of Gene Linkage
, 2007
"... Genetic algorithm (GA) is expected to realize black box optimization, which can solve optimization problems based only on the values of objective functions. Efficient building block mixing is essential in genetic algorithms. For simple GAs, it is not an easy task without prior knowledge of a proble ..."
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Genetic algorithm (GA) is expected to realize black box optimization, which can solve optimization problems based only on the values of objective functions. Efficient building block mixing is essential in genetic algorithms. For simple GAs, it is not an easy task without prior knowledge of a problem and such knowledge is not always available. GAs which can learn or detect problem structure automatically are called competent genetic algorithms (cGAs). This dissertation proposes two important parts to realize cGAs, (1) a novel approach to identify linkages and (2) a crossover for functions with complexly overlapping building blocks. First, we propose a novel linkage identification method called Dependency Detection for Distribution Derived from fitness Differences (D5), which detects linkage by estimating strings clustered according to fitness differences caused by perturbations. It is important to detect linkage — interaction between variables tightly linked to form a building block — to process building blocks effectively. The D5 inherits the merits of two classes of cGAs, estimation of distribution algorithms (EDAs) and perturbation methods (PMs), that is, it can detect linkages for problems which are difficult for EDAs requiring smaller computational cost than PMs. In addition, Context-Dependent Crossover (CDC) has been developed to combine complexly overlapping building blocks. The CDC examine contexts of each pair of strings in addition to the linkage information to process building blocks. Combining the linkage identification and the crossover methods, we have realized a competent genetic algorithm applicable to wider-spectrum real-world problems.
PERFORMANCE ANALYSIS OF LINKAGE LEARNING TECHNIQUES IN GENETIC ALGORITHMS
"... One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the efficiencies of Simple Genetic Algorithm (SGA) while solving NP hard Problems. Discovery of Linkage Learning Technique is an important task in GA. Almost all existing Linkage Learning Techniques follow eit ..."
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One variance of Genetic Algorithms is a Linkage Learning Genetic Algorithm (LLGA) enhances the efficiencies of Simple Genetic Algorithm (SGA) while solving NP hard Problems. Discovery of Linkage Learning Technique is an important task in GA. Almost all existing Linkage Learning Techniques follow either random approach or probabilistic approaches. This makes repeated passes over the population to determine the relationship between individuals. SGA with random linkage technique is simple but may take long time to converge to the optimal solutions. This paper uses a linkage learning operator called Gene Silencing which is an inspired mechanism from biological systems. The Gene Silencing mechanism is used to improve the linkages by preserving the building blocks in an individual from the disruption of recombination processes such as Crossover and Mutation. It converges quickly to the optimal solution without compromising the diversification on search spaces. To prove this phenomenon, the Travelling Sales Person problem (TSP) has been chosen to retain the order of cities in a tour. Experiments carried out on different TSP benchmark instances taken from TSPLIB which is a standard library for TSP problems. These benchmark instances have also been applied on various linkage learning techniques and analyses the performance of these techniques with Gene Silencing (GS) mechanism. The performance analysis has been made on experimental results with respect to optimal solution and convergence
