Results 1  10
of
56
A Genetic Algorithm Tutorial
 Statistics and Computing
, 1994
"... This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorit ..."
Abstract

Cited by 231 (5 self)
 Add to MetaCart
This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic algorithms are reviewed, include the schema theorem as well as recently developed exact models of the canonical genetic algorithm.
A Survey of Parallel Genetic Algorithms
 CALCULATEURS PARALLELES, RESEAUX ET SYSTEMS REPARTIS
, 1998
"... Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey att ..."
Abstract

Cited by 147 (5 self)
 Add to MetaCart
Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. As such, there has been extensive research in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the survey focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multipopulation parallel GAs and presents some recent advancements.
Fundamental Principles of Deception in Genetic Search
 Foundations of Genetic Algorithms
, 1991
"... This paper presents several theorems concerning the nature of deception and the central role that deception plays in function optimization using genetic algorithms. A simple proof is offered which shows that the only problems which pose challenging optimization tasks are problems that involve so ..."
Abstract

Cited by 95 (5 self)
 Add to MetaCart
This paper presents several theorems concerning the nature of deception and the central role that deception plays in function optimization using genetic algorithms. A simple proof is offered which shows that the only problems which pose challenging optimization tasks are problems that involve some degree of deception and which result in conflicting karm bandit competitions between hyperplanes. The concept of a deceptive attractor is introduced and shown to be more general than the deceptive optimum found in the deceptive functions that have been constructed to date. Also introduced are the concepts of fully deceptive problems as well as less strict consistently deceptive problems. A proof is given showing that deceptive attractors must have a complementary bit pattern to that found in the binary representation of the global optimum if a function is to be either fully deceptive or consistently deceptive. Some empirical results are presented which demonstrate different methods of dealing with deception and poor linkage during genetic search.
A Parallel Genetic Algorithm for the Set Partitioning Problem
, 1994
"... In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed stea ..."
Abstract

Cited by 66 (1 self)
 Add to MetaCart
In this dissertation we report on our efforts to develop a parallel genetic algorithm and apply it to the solution of the set partitioning problema difficult combinatorial optimization problem used by many airlines as a mathematical model for flight crew scheduling. We developed a distributed steadystate genetic algorithm in conjunction with a specialized local search heuristic for solving the set partitioning problem. The genetic algorithm is based on an island model where multiple independent subpopulations each run a steadystate genetic algorithm on their own subpopulation and occasionally fit strings migrate between the subpopulations. Tests on forty realworld set partitioning problems were carried out on up to 128 nodes of an IBM SP1 parallel computer. We found that performance, as measured by the quality of the solution found and the iteration on which it was found, improved as additional subpopulations were added to the computation. With larger numbers of subpopulations the genetic algorithm was regularly able to find the optimal solution to problems having up to a few thousand integer variables. In two cases, highquality integer feasible solutions were found for problems with 36,699 and 43,749 integer variables, respectively. A notable limitation we found was the difficulty solving problems with many constraints.
A Summary of Research on Parallel Genetic Algorithms
, 1995
"... The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publi ..."
Abstract

Cited by 63 (2 self)
 Add to MetaCart
The main goal of this paper is to summarize the previous research on parallel genetic algorithms. We present an extension to previous categorizations of the parallelization techniques used in this field. We will use this categorization to guide us through a review of many of the most important publications. We will build on this survey to try to identify some of the problems that have not been studied systematically yet. 1 Introduction Genetic Algorithms (GAs) are efficient search methods based on principles of natural selection and population genetics. They are being successfully applied to problems in business, engineering and science (Goldberg, 1994). GAs use randomized operators operating over a population of candidate solutions to generate new points in the search space. In the past few years, parallel genetic algorithms (PGAs) have been used to solve difficult problems. Hard problems need a bigger population and this translates directly into higher computational costs. The basic...
Evaluationrelaxation schemes for genetic and evolutionary algorithms
, 2002
"... Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by th ..."
Abstract

Cited by 60 (28 self)
 Add to MetaCart
Genetic and evolutionary algorithms have been increasingly applied to solve complex, large scale search problems with mixed success. Competent genetic algorithms have been proposed to solve hard problems quickly, reliably and accurately. They have rendered problems that were difficult to solve by the earlier GAs to be solvable, requiring only a subquadratic number of function evaluations. To facilitate solving largescale complex problems, and to further enhance the performance of competent GAs, various efficiencyenhancement techniques have been developed. This study investigates one such class of efficiencyenhancement technique called evaluation relaxation. Evaluationrelaxation schemes replace a highcost, lowerror fitness function with a lowcost, higherror fitness function. The error in fitness functions comes in two flavors: Bias and variance. The presence of bias and variance in fitness functions is considered in isolation and strategies for increasing efficiency in both cases are developed. Specifically, approaches for choosing between two fitness functions with either differing variance or differing bias values have been developed. This thesis also investigates fitness inheritance as an evaluation
An Overview of Evolutionary Algorithms: Practical Issues and Common Pitfalls
 Information and Software Technology
, 2001
"... An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimi ..."
Abstract

Cited by 34 (0 self)
 Add to MetaCart
An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and realvalued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed.
The Island Model Genetic Algorithm: On Separability, Population Size and Convergence
 Journal of Computing and Information Technology
, 1998
"... Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic di ..."
Abstract

Cited by 33 (0 self)
 Add to MetaCart
Parallel Genetic Algorithms have often been reported to yield better performance than Genetic Algorithms which use a single large panmictic population. In the case of the Island Model genetic algorithm, it has been informally argued that having multiple subpopulations helps to preserve genetic diversity, since each island can potentially follow a different search trajectory through the search space. It is also possible that since linearly separable problems are often used to test Genetic Algorithms, that Island Models may simply be particularly well suited to exploiting the separable nature of the test problems. We explore this possibility by using the infinite population models of simple genetic algorithms to study how Island Models can track multiple search trajectories. We also introduce a simple model for better understanding when Island Model genetic algorithms may have an advantage when processing some test problems. We provide empirical results for both linearly separa...
Delta Coding: An Iterative Search Strategy for Genetic Algorithms
 Proceedings of the Fourth International Conference on Genetic Algorithms
, 1991
"... A new search strategy for genetic algorithms is introduced which allows iterative searches with complete reinitialization of the population preserving the progress already made toward solving an optimization task. Delta coding is a simple search strategy based on the idea that the encoding used ..."
Abstract

Cited by 32 (1 self)
 Add to MetaCart
A new search strategy for genetic algorithms is introduced which allows iterative searches with complete reinitialization of the population preserving the progress already made toward solving an optimization task. Delta coding is a simple search strategy based on the idea that the encoding used by a genetic algorithm can express a distance away from some previous partial solution. Delta values are added to a partial solution before evaluating the fitness; the delta encoding forms a new hypercube of equal or smaller size that is constructed around the most recent partial solution.