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NonLinear Genetic Representations
, 1992
"... The limitations of linear chromosomes and conventional recombination operators are reviewed. It is argued that there are at least three classes of problems for which such representations and operators are likely to be ineffective. Methods for constructing operators which manipulate more complex stru ..."
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Cited by 32 (2 self)
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The limitations of linear chromosomes and conventional recombination operators are reviewed. It is argued that there are at least three classes of problems for which such representations and operators are likely to be ineffective. Methods for constructing operators which manipulate more complex structures with evolutionary search methods are presented, and it is argued that whenever possible, genetic operators and analogues of schemata should be defined directly in space of phenotypes, rather than in the genotype (representation) space.
RPL2: A Language and Parallel Framework for Evolutionary Computing
 PARALLEL PROBLEM SOLVING FROM NATURE III, LNCS 866
, 1994
"... The Reproductive Plan Language 2 (RPL2) is an extensible interpreted language for writing and using evolutionary computing programs. It supports arbitrary genetic representations, all structured population models described in the literature together with further hybrids, and runs on parallel or s ..."
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Cited by 13 (7 self)
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The Reproductive Plan Language 2 (RPL2) is an extensible interpreted language for writing and using evolutionary computing programs. It supports arbitrary genetic representations, all structured population models described in the literature together with further hybrids, and runs on parallel or serial hardware while hiding parallelism from the user. This paper surveys structured population models, explains and motivates the benefits of generic systems such as RPL2 and describes the suite of applications that have used it to date.
Cooperation through Hierarchical Competition in Genetic Data Mining
, 1994
"... Data mining is the automatic search for interesting and useful correlations within large databases. It lays particular emphasis on discovering patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. This paper describes a formulation of ..."
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Cited by 10 (1 self)
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Data mining is the automatic search for interesting and useful correlations within large databases. It lays particular emphasis on discovering patterns that are hard or impossible to detect using standard query mechanisms and classical statistical techniques. This paper describes a formulation of data mining as a welldefined search problem and shows how a hierarchical implementation of a genetic algorithm can be used to execute this task. Such a data miner has been implemented using RPL2, a language and parallel framework for evolutionary computation. The resulting system has been used to search a retail sales database from a major highstreet chain, successfully producing interesting characterisations of consumer behaviour. The hierarchical genetic algorithm introduced is of relevance not only to data mining, but also to general covering problems.
The Reproductive Plan Language RPL2: Motivation, Architecture and Applications
 GENETIC ALGORITHMS IN OPTIMISATION, SIMULATION AND MODELLING. IOS
, 1994
"... The reproductive plan language RPL2 is a computer language designed to facilitate the writing, execution and modification of evolutionary algorithms. It provides a number of data parallel constructs appropriate to evolutionary computing, facilitating the building of efficient parallel interpret ..."
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Cited by 9 (3 self)
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The reproductive plan language RPL2 is a computer language designed to facilitate the writing, execution and modification of evolutionary algorithms. It provides a number of data parallel constructs appropriate to evolutionary computing, facilitating the building of efficient parallel interpreters and compilers. This facility is exploited by the current interpreted implementation. RPL2 supports all current structured population models and their hybrids at language level. Users can extend the system by linking against the supplied framework Ccallable functions, which may then be invoked directly from an RPL2 program. There are no restrictions on the form of genomes, making the language particularly well suited to realworld optimisation problems and the production of hybrid algorithms. This paper describes the theoretical and practical considerations that shaped the design of RPL2, the language, interpreter and runtime system built, and a suite of industrial applications...
A Study in Set Recombination
, 1993
"... A family of problems for which the solution is a fixed size set is studied, using fitness functions with varying degrees of epistasis. An empirical comparison between a traditional crossover operator with a binary representation and a penalty function, and the representationindependent Random Assort ..."
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Cited by 9 (1 self)
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A family of problems for which the solution is a fixed size set is studied, using fitness functions with varying degrees of epistasis. An empirical comparison between a traditional crossover operator with a binary representation and a penalty function, and the representationindependent Random Assorting Recombination Operator (RAR) is performed. RAR is found to perform marginally better in all cases. Since RAR is a parameterised operator, a study of the effect of varying its parameter, which can control any tradeoff between respect and assortment, is also presented. 1 Introduction Throughoutthe short history of genetic algorithms there has been a creative tension between two quite distinct schools of thought on the subjects of representations and operators. The fundamental disagreement concerns the relative merits of simple, low cardinality representations used with standard recombination operatorstypically "npoint" and more recently uniform crossover 1 and more complex, prob...
EpccTr9411
"... The application of a genetic algorithm to an industrial pipesizing problem is presented. The optimum sizes for the pipes in a given network with specified supply and demand requirements must be determined, subject to two additional constraints. The problem was used as a test case for the evoluti ..."
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The application of a genetic algorithm to an industrial pipesizing problem is presented. The optimum sizes for the pipes in a given network with specified supply and demand requirements must be determined, subject to two additional constraints. The problem was used as a test case for the evolutionary computing language RPL2. The genetic approach is shown to produce better results than the existing industrial heuristic at the expense of longer run times.
Kingâ€™s Buildings
"... A family of problems for which the solution is a fixed size set is studied, using fitness functions with varying degrees of epistasis. An empirical comparison between a traditional crossover operator with a binary representation and a penalty function, and the representationindependent Random Ass ..."
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A family of problems for which the solution is a fixed size set is studied, using fitness functions with varying degrees of epistasis. An empirical comparison between a traditional crossover operator with a binary representation and a penalty function, and the representationindependent Random Assorting Recombination Operator (RAR) is performed. RAR is found to perform marginally better in all cases. Since RAR is a parameterised operator, a study of the effect of varying its parameter, which can control any tradeoff between respect and assortment, is also presented. 1
Constrained Gas Network Pipe Sizing with Genetic Algorithms
, 1994
"... The application of a genetic algorithm to an industrial pipesizing problem is presented. The optimum sizes for the pipes in a given network with specified supply and demand requirements must be determined, subject to two additional constraints. The problem was used as a test case for the evoluti ..."
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The application of a genetic algorithm to an industrial pipesizing problem is presented. The optimum sizes for the pipes in a given network with specified supply and demand requirements must be determined, subject to two additional constraints. The problem was used as a test case for the evolutionary computing language RPL2. The genetic approach is shown to produce better results than the existing industrial heuristic at the expense of longer run times. 1 Problem Specification It is a frequent criticism of genetic algorithms and evolutionary computation more generally that published problems are usually contrived and unconstrained. (There are of course notable exceptions, for instance the work of Goldberg (1989) on gas network compressor optimisation.) This paper demonstrates how a relatively straightforward implementation of a genetic algorithm can find significantly better solutions than the standard heuristics actually used for a real, constrained pipesizing problem to offe...