Publications (9)0 Total impact
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Article: Simple Feasibility Rules and Differential
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ABSTRACT: In this paper, we propose a differential evolution algorithm to solve constrained optimization problems. Our approach uses three simple selection criteria based on feasibility to guide the search to the feasible region. The proposed approach does not require any extra parameters other than those normally adopted by the Differential Evolution algorithm. The present approach was validated using test functions from a well-known benchmark commonly adopted to validate constraint-handling techniques used with evolutionary algorithms. The results obtained by the proposed approach are very competitive with respect to other constraint-handling techniques that are representative of the state-of-the-art in the area.08/2004; -
Article: Hybridizing a genetic algorithm with an artificial immune system for global optimization
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ABSTRACT: In this paper we propose an algorithm based on a model of the immune system to handle constraints of all types (linear, nonlinear, equality and in-equality) in a genetic algorithm used for global optimization. The approach is implemented both in serial and parallel forms, and it is validated using sev-eral test functions taken from the specialized literature. Our results indicate that the proposed approach is highly competitive with respect to penalty-based techniques and with respect to other constraint-handling techniques which are considerably more complex to implement.03/2004; -
Article: A Parallel Implementation of an Arti
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ABSTRACT: In this paper, we present a parallel version of a constraint-handling technique based on the arti cial immune system. The proposed approach does not require penalty factors of any kind, it is relatively simple to implement and it is quite competitive with more sophisticated techniques. Additionally, when parallelized using an island scheme, the approach not only reduces its computational time, but it also improves the quality of the results produced.02/2004; -
Article: Design of Combinational Logic Circuits through an Evolutionary Multiobjective Optimization
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ABSTRACT: In this paper, we propose a population-based evolutionoxy multiobjective optimization approach to design combinationa! circuits. Our results indicate that the proposed approach cn significantly reduce the computational effort required by a genetic algorithm (GA) to design circuits at a gate level while generating equivalent or even better solutions (i.e., circuits with a lower number of gates) than a human designer or even other GAs. Severa examples taken from the literature oxe used to evaluate the performance of the proposed approach.07/2003; -
Article: A Numerical Comparison of some Multiobjective-Based Techniques to Handle Constraints in Genetic Algorithms
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ABSTRACT: This paper discusses the general multiobjective optimization concepts that can and have been used to incorporate constraints of any type (linear, nonlinear, equality and inequality) into the tness function of a genetic algorithm used for global optimization. We also describe several approaches currently reported in the literature and four of them are compared using several test functions. The results obtained are widely discussed and some ideas about how to devise new approaches based on multiobjective optimization concepts are also briey analyzed.12/2002; -
Article: Handling Constraints in Genetic Algorithms using Dominance-Based Tournaments
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ABSTRACT: In this paper, we propose a constraint-handling approach for genetic algorithms which uses a dominance-based selection scheme. The proposed approach does not require the fine tuning of a penalty function and does not require extra mechanisms to maintain diversity in the population. The algorithm is validated using several test functions taken from the specialized literature on evolutionary optimization. The results obtained indicate that the approach can produce reasonable results at low computational costs.05/2002; -
Article: Theoretical and Numerical Constraint-Handling Techniques used with Evolutionary Algorithms: A Survey of the State of the Art
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ABSTRACT: This paper provides a comprehensive survey of the most popular constraint-handling techniques currently used with evolutionary algorithms. We review approaches that go from simple variations of a penalty function, to others, more sophisticated, that are biologically inspired on emulations of the immune system, culture or ant colonies. Besides describing briefly each of these approaches (or groups of techniques), we provide some criticism regarding their highlights and drawbacks. A small comparative study is also conducted, in order to assess the performance of several penalty-based approaches with respect to a dominance-based technique proposed by the author, and with respect to some mathematical programming approaches. Finally, we provide some guidelines regarding how to select the most appropriate constraint-handling technique for a certain application, ad we conclude with some of the the most promising paths of future research in this area.10/2001; -
Article: A Short Tutorial on Evolutionary Multiobjective Optimization
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ABSTRACT: This tutorial will review some of the basic concepts related to evolutionary multiobjective optimization (i.e., the use of evolutionary algorithms to handle more than one objective function at a time). The most commonly used evolutionary multiobjective optimization techniques will be described and criticized, including some of their applications. Theory, test functions and metrics will be also discussed. Finally, we will provide some possible paths of future research in this area.09/2001; -
Article: A Micro-Genetic Algorithm for Multiobjective Optimization
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ABSTRACT: In this paper, we propose a multiobjective optimization approach based on a micro genetic algorithm (micro-GA) which is a genetic algorithm with a very small population (four individuals were used in our experiment) and a reinitialization process. We use three forms of elitism and a memory to generate the initial population of the micro-GA. Our approach is tested with several standard functions found in the specialized literature. The results obtained are very encouraging, since they show that this simple approach can produce an important portion of the Pareto front at a very low computational cost.08/2001;
