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Informed operators: Speeding up genetic-algorithm-based design optimization using reduced models
- In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO
, 2000
"... In this paper we describe a method for improving genetic-algorithm-based optimization using informed genetic operators. The idea is to make the genetic operators such as mutation and crossover more informed using reduced models. In every place where a random choice is made, for example when a ..."
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In this paper we describe a method for improving genetic-algorithm-based optimization using informed genetic operators. The idea is to make the genetic operators such as mutation and crossover more informed using reduced models. In every place where a random choice is made, for example when a point is mutated, instead of generating just one random mutation we generate several, rank them using a reduced model, then take the best to be the result of the mutation. The proposed method is particularly suitable for search spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed method can significantly speed up the GA optimizer. 1 Introduction This paper concerns the application of Genetic Algorithms (GAs) in realistic engineering design domains. In such domains a design is represented by a number of continuous design parameters, so that potential solutions are vec...
Constrained Multi-Objective Optimization Using Steady State Genetic Algorithms
- In Proceedings of Genetic and Evolutionary Computation Conference
, 2003
"... In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs. ..."
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Cited by 16 (0 self)
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In this paper we propose two novel approaches for solving constrained multi-objective optimization problems using steady state GAs.
An Incremental-Approximate-Clustering Approach for Developing Dynamic Reduced Models for Design Optimization
- Proceedings of IEEE Congress on Evolutionary Computation
, 2000
"... In this paper we describe a method for improving genetic-algorithm-based optimization using reduced models. The main idea is to maintain a large sample of the points encountered in the course of the optimization divided into clusters. Least squares quadratic approximations are periodically formed o ..."
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Cited by 15 (6 self)
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In this paper we describe a method for improving genetic-algorithm-based optimization using reduced models. The main idea is to maintain a large sample of the points encountered in the course of the optimization divided into clusters. Least squares quadratic approximations are periodically formed of the entire sample as well as the big enough clusters. These approximations are used as a reduced model to compute cheap approximations of the fitness function through a two phase approach in which the point is first classified (into potentially feasible, infeasible or unevaluable) and then its fitness is computed accordingly. We then use the reduced models to speedup the GA optimization by making the genetic operators such as mutation and crossover more informed. The proposed approach is particularly suitable for search spaces with expensive evaluation functions, such as arise in engineering design. Empirical results in several engineering design domains demonstrate that the proposed metho...
Guided Crossover: A New Operator for Genetic Algorithm Based Optimization
- In Proceedings of the Congress on Evolutionary Computation
, 1997
"... Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a ten ..."
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Cited by 7 (5 self)
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Genetic algorithms (GAs) have been extensively used in different domains as a means of doing global optimization in a simple yet reliable manner. They have a much better chance of getting to global optima than gradient based methods which usually converge to local sub optima. However, GAs have a tendency of getting only moderately close to the optima in a small number of iterations. To get very close to the optima, the GA needs a very large number of iterations. Whereas gradient based optimizers usually get very close to local optima in a relatively small number of iterations. In this paper we describe a new crossover operator which is designed to endow the GA with gradient-like abilities without actually computing any gradients and without sacrificing global optimality. The operator works by using guidance from all members of the GA population to select a direction for exploration. Empirical results in two engineering design domains and across both binary and floating point representa...
Comparison of methods for developing dynamic reduced models for design optimization
- In Proceedings of the Congress on Evolutionary Computation (CEC’2002
, 2002
"... Abstract- In this paper we compare three methods for forming reduced models to speed up geneticalgorithm-based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization by making the genetic operators more informed. Emp ..."
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Cited by 4 (1 self)
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Abstract- In this paper we compare three methods for forming reduced models to speed up geneticalgorithm-based optimization. The methods work by forming functional approximations of the fitness function which are used to speed up the GA optimization by making the genetic operators more informed. Empirical results in several engineering design domains are presented. I.
Data driven design optimization methodology: A dynamic data driven application system
- Proceedings of International Conference of Computational Science - ICCS’03, Peter M.A. Sloot, et al. (Eds.) Melbourne Australia, June 2-4, LNCS 2660,Part IV
, 2003
"... Abstract. Engineering design optimization using concurrent integrated experiment and simulation is a Dynamic Data Driven Application System (DDDAS) wherein remote experiment and simulation can be synergistically utilized in real-time to achieve better designs in less time than conventional methods. ..."
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Abstract. Engineering design optimization using concurrent integrated experiment and simulation is a Dynamic Data Driven Application System (DDDAS) wherein remote experiment and simulation can be synergistically utilized in real-time to achieve better designs in less time than conventional methods. The paper describes the Data Driven Design Optimization Methodology (DDDOM) being developed for engineering design optimization.
ASAGA: An Adaptive Surrogate-Assisted Genetic Algorithm
"... Genetic algorithms (GAs) used in complex optimization domains usually need to perform a large number of fitness function evaluations in order to get near-optimal solutions. In real world application domains such as the engineering design problems, such evaluations might be extremely expensive comput ..."
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Genetic algorithms (GAs) used in complex optimization domains usually need to perform a large number of fitness function evaluations in order to get near-optimal solutions. In real world application domains such as the engineering design problems, such evaluations might be extremely expensive computationally. It is therefore common to estimate or approximate the fitness using certain methods. A popular method is to construct a so called surrogate or meta-model to approximate the original fitness function, which can simulate the behavior of the original fitness function but can be evaluated much faster. It is usually difficult to determine which approximate model should be used and/or what the frequency of usage should be. The answer also varies depending on the individual problem. To solve this problem, an adaptive fitness approximation GA (ASAGA) is presented. ASAGA adaptively chooses the appropriate model type; adaptively adjusts the model complexity and the frequency of model usage according to time spent and model accuracy. ASAGA also introduces a stochastic penalty function method to handle constraints. Experiments show that ASAGA outperforms non-adaptive surrogate-assisted GAs with statistical significance.
Using singular value decomposition to improve a genetic algorithm’s performance
- In Proceedings of the 2003 Congress on Evolutionary Computation CEC2003
, 2003
"... Abstract- The focus of this work is to investigate the effects of applying the singular value decomposition (SVD), a linear algebra technique, to the domain of Genetic Algorithms. Empirical evidence, concerning document comparison, suggests that the SVD can be used to model information in such a way ..."
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Abstract- The focus of this work is to investigate the effects of applying the singular value decomposition (SVD), a linear algebra technique, to the domain of Genetic Algorithms. Empirical evidence, concerning document comparison, suggests that the SVD can be used to model information in such a way that provides both a saving in storage space and an improvement in information retrieval. It will be shown that these beneficial properties can be extended to many other different types of comparison as well. Briefly, vectors representing the genes of individuals are projected into a new low-dimensional space, obtained by the singular value decomposition of a gene-individual matrix. The information about what it means to be a good or bad individual serves as a basis for qualifying candidate individuals for reinsertion into the next generation. Positive results from different approaches of this application are presented and evaluated. In addition, several possible alternative techniques are proposed and considered. expose the most strikinz similarities between a given individual and a strategically chosen population of individuals. These similarities are used to influence the direction of the GAS search process by qualifying candidate individuals for reinsertion into the next generation based on their proximity to other individuals, whose fitnesses have already been computed. Initial results from the application of this process indicate significant improvements in the CA’s performance. The intent is to evaluate several different tested approaches of using SVD qualifiers to enhance the performance of GAS, justify any apparent performance improvement over traditional GAS, and to speculate about other ways of using SVD in GAS. In the remainder of this paper we provide some background information in Section 2, followed by a description of the proposed method for SVD integration into a genetic algorithm in Section 3. Section 4 describes the results achieved by using the proposed method in several different problem domains. Finally. Section 5 provides some promising opportunities for future research.
Complementary Selection and Variation for an Efficient Multiobjective Optimization of Complex Systems
- GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE GECCO 04, LBP
, 2004
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