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73
Multi-Objective Bayesian Optimization Algorithm
- in Proceedings of the Genetic and Evolutionary Computation Conference
, 2002
"... This paper proposes a competent multi-objective genetic algorithm called the multiobjective Bayesian optimization algorithm (mBOA). mBOA incorporates the selection method of the non-dominated sorting genetic algorithm-II (NSGA-II) into the Bayesian optimization algorithm (BOA). The proposed algorith ..."
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Cited by 18 (4 self)
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This paper proposes a competent multi-objective genetic algorithm called the multiobjective Bayesian optimization algorithm (mBOA). mBOA incorporates the selection method of the non-dominated sorting genetic algorithm-II (NSGA-II) into the Bayesian optimization algorithm (BOA). The proposed algorithm has been tested on an array of test functions which incorporate deception and loose-linkage and the results are compared to those of NSGA-II. Results indicate that mBOA outperforms NSGA-II on large loosely linked deceptive problems.
Compressed Introns in a Linkage Learning Genetic Algorithm
, 1998
"... Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This paper revisits and extends the latest of such efforts--- the linkage learning genetic algorithm. Specifically, it introduces an efficient mechanism for representing the noncoding material. Recent invest ..."
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Cited by 17 (6 self)
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Over the last 10 years, many efforts have been made to design a competent genetic algorithm. This paper revisits and extends the latest of such efforts--- the linkage learning genetic algorithm. Specifically, it introduces an efficient mechanism for representing the noncoding material. Recent investigations suggest that this new method is crucial for solving a large class of hard optimization problems. 1 Introduction The simple genetic algorithm (SGA) has been applied successfully in a variety of applications, including medical, financial, and all kinds of engineering problems. Its power comes from its ability to combine good pieces (building blocks) from different solutions and assemble them into a single super solution. But despite their success, there are still problems whose solution can be constructed by the juxtaposition of building blocks, and yet the SGA fails. The reason behind this failure is well understood and is due to the socalled linkage problem. Before applying a ge...
Operator Adaptation in Evolutionary Computation and its Application to Structure Optimization of Neural Networks
, 2001
"... In this study, we give a brief overview of search strategy adaptation in evolutionary computation. The ..."
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Cited by 17 (7 self)
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In this study, we give a brief overview of search strategy adaptation in evolutionary computation. The
Simulating Evolution with a Computational Model of Embryogeny
, 2006
"... Natural evolution is an incredibly complex dynamical system which we are still grasping to understand. One could argue that the evolutionary search process itself is not the most important part of evolution, but rather it is the evolved representational structures and physical implementations of the ..."
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Cited by 16 (1 self)
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Natural evolution is an incredibly complex dynamical system which we are still grasping to understand. One could argue that the evolutionary search process itself is not the most important part of evolution, but rather it is the evolved representational structures and physical implementations of the genotype-phenotype mappings that make it so powerful. One such behaviour is embryogeny, the process by which genetic representations within cells control the development of a multi-cellular organism. Evolution of complex organisms is highly dependent upon biological mechanisms such as embryogeny, and this has fundamental consequences. The objective of this work is to investigate these consequences for a simulated evolutionary search process using a computational model of embryogeny. The model simulates the dynamics achieved within biological systems between the genetic representation of an individual and the mapping to its phenotypic representation. It is not an attempt to produce a biologically accurate or plausible model of the cell, growth or genetics. The model is used to create individuals which develop into multi-component patterns
Incremental Commitment in Genetic Algorithms
- Proceedings of GECCO
, 1999
"... Successful recombination in the simple GA requires that interdependent genes be close to each other on the genome. Several methods have been proposed to reorder genes on the genome when the given ordering is unfavorable. The Messy GA (MGA) is one such ‘moving-locus ’ scheme. However, gene reordering ..."
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Cited by 15 (8 self)
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Successful recombination in the simple GA requires that interdependent genes be close to each other on the genome. Several methods have been proposed to reorder genes on the genome when the given ordering is unfavorable. The Messy GA (MGA) is one such ‘moving-locus ’ scheme. However, gene reordering is only part of the Messy picture. The MGA uses another mechanism that is influential in enabling successful recombination. Specifically, the use of partial specification (or variable length genomes) allows the individuals themselves, rather than the ordering of genes within an individual, to represent which genes ‘go together’ during recombination. This paper examines this critical feature of the MGA and illustrates the impact that partial specification has on recombination. We formulate an Incremental Commitment GA that uses partially specified representations and recombination inspired by the MGA but separates these features from the moving-locus aspects and many of the other features of the existing algorithm. 1
Genetic Planning Using Variable Length Chromosomes
- In Proc. ICAPS
, 2005
"... This paper describes a genetic planning system, i.e., a program capable of solving planning problems us-ing evolutionary techniques. As opposed to other ap-proaches in Genetic Planning, we use a variable length chromosomes model in addition to a complex tness function and several enhancements of the ..."
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Cited by 14 (0 self)
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This paper describes a genetic planning system, i.e., a program capable of solving planning problems us-ing evolutionary techniques. As opposed to other ap-proaches in Genetic Planning, we use a variable length chromosomes model in addition to a complex tness function and several enhancements of the Simple Ge-netic Algorithm (Holland 1975), such as multipopula-tions, population reset, weak memetism, tournament se-lection and elitist genetic operators. Our genetic planner is tested on standard planning domains and problems (described in PDDL), is used for parameter and per-formance analysis, and is compared to previous work. Results show efciency in memory management and greater solving power than the predecessors'.
A Prescriptive Formalism for Constructing Domain-specific Evolutionary Algorithms
, 1998
"... It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, trad ..."
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Cited by 14 (0 self)
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It has been widely recognised in the computational intelligence and machine learning communities that the key to understanding the behaviour of learning algorithms is to understand what representation is employed to capture and manipulate knowledge acquired during the learning process. However, traditional evolutionary algorithms have tended to employ a fixed representation space (binary strings), in order to allow the use of standardised genetic operators. This approach leads to complications for many problem domains, as it forces a somewhat artificial mapping between the problem variables and the canonical binary representation, especially when there are dependencies between problem variables (e.g. problems naturally defined over permutations). This often obscures the relationship between genetic structure and problem features, making it difficult to understand the actions of the standard genetic operators with reference to problem-specific structures. This thesis instead advocates m...
Self Adaptation in Evolutionary Algorithms
, 1998
"... Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via ..."
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Cited by 13 (1 self)
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Evolutionary Algorithms are search algorithms based on the Darwinian metaphor of “Natural Selection”. Typically these algorithms maintain a population of individual solutions, each of which has a fitness attached to it, which in some way reflects the quality of the solution. The search proceeds via the iterative generation, evaluation and possible incorporation of new individuals based on the current population, using a number of parameterised genetic operators. In this thesis the phenomenon of Self Adaptation of the genetic operators is investigated. A new framework for classifying adaptive algorithms is proposed, based on the scope of the adaptation, and on the nature of the transition function guiding the search through the space of possible configurations of the algorithm. Mechanisms are investigated for achieving the self adaptation of recombination and mutation operators within a genetic algorithm, and means of combining them are investigated. These are shown to produce significantly better results than any of the combinations of fixed operators tested, across a range of problem types. These new operators reduce the need for the designer of an algorithm to select
Introducing Start Expression Genes to the Linkage Learning Genetic Algorithm
- Parallel Problem Solving from Nature, 7 , 351–360. (Also IlliGAL
, 2002
"... This paper discusses the use of start expression genes and a modified exchange crossover operator in the linkage learning genetic algorithm (LLGA) that enables the genetic algorithm to learn the linkage of building blocks (BBs) through probabilistic expression (PE). The difficulty that the original ..."
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Cited by 10 (7 self)
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This paper discusses the use of start expression genes and a modified exchange crossover operator in the linkage learning genetic algorithm (LLGA) that enables the genetic algorithm to learn the linkage of building blocks (BBs) through probabilistic expression (PE). The difficulty that the original LLGA encounters is shown with empirical results. Based on the observation, start expression genes and a modified exchange crossover operator are proposed to enhance the ability of the original LLGA to separate BBs and to improve LLGA's performance on uniformly scaled problems. The effect of the modifications is also presented in the paper.
Linkage Learning, Overlapping Building Blocks, and Systematic Strategy for Scalable Recombination
"... This paper aims at an important, but poorly studied area in genetic algorithm (GA) field: How to design the crossover operator for problems with overlapping building blocks (BBs). To investigate this issue systematically, the relationship between an inaccurate linkage model and the convergence time ..."
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Cited by 9 (4 self)
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This paper aims at an important, but poorly studied area in genetic algorithm (GA) field: How to design the crossover operator for problems with overlapping building blocks (BBs). To investigate this issue systematically, the relationship between an inaccurate linkage model and the convergence time of GA is studied. Specifically, the effect of the error of so-called false linkage is analogized to a lower exchange probability of uniform crossover. The derived qualitative convergence-time model is used to develop a scalable recombination strategy for problems with overlapping BBs. A set of problems with circularly overlapping BBs exemplify the recombination strategy.
