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A Comprehensive Survey of EvolutionaryBased Multiobjective Optimization Techniques
 Knowledge and Information Systems
, 1998
"... . This paper presents a critical review of the most important evolutionarybased multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search cap ..."
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Cited by 211 (19 self)
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. This paper presents a critical review of the most important evolutionarybased multiobjective optimization techniques developed over the years, emphasizing the importance of analyzing their Operations Research roots as a way to motivate the development of new approaches that exploit the search capabilities of evolutionary algorithms. Each technique is briefly described mentioning its advantages and disadvantages, their degree of applicability and some of their known applications. Finally, the future trends in this discipline and some of the open areas of research are also addressed. Keywords: multiobjective optimization, multicriteria optimization, vector optimization, genetic algorithms, evolutionary algorithms, artificial intelligence. 1 Introduction Since the pioneer work of Rosenberg in the late 60s regarding the possibility of using geneticbased search to deal with multiple objectives, this new area of research (now called evolutionary multiobjective optimization) has grown c...
Using Genetic Algorithms to Solve NPComplete Problems
, 1989
"... A strategy for using Genetic Algorithms (GAs) to solve NPcomplete problems is presented. The key aspect of the approach taken is to exploit the observation that, although all NPcomplete problems are equally difficult in a general computational sense, some have much better GA representations than o ..."
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Cited by 131 (5 self)
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A strategy for using Genetic Algorithms (GAs) to solve NPcomplete problems is presented. The key aspect of the approach taken is to exploit the observation that, although all NPcomplete problems are equally difficult in a general computational sense, some have much better GA representations than others, leading to much more successful use of GAs on some NPcomplete problems than on others. Since any NPcomplete problem can be mapped into any other one in polynomial time, the strategy described here consists of identifying a canonical NPcomplete problem on which GAs work well, and solving other NPcomplete problems indirectly by mapping them onto the canonical problem. Initial empirical results are presented which support the claim that the Boolean Satisfiability Problem (SAT) is a GAeffective canonical problem, and that other NPcomplete problems with poor GA representations can be solved efficiently by mapping them first onto SAT problems. 1. Introduction One approach to discussin...
Genetic algorithms for tracking changing environments
 Proceedings of the Fifth International Conference on Genetic Algorithms
, 1993
"... In this paper, we explore the use of alternative mutation strategies as a means of increasing diversity so that the GA can track the optimum of a changing environment. This paper contrasts three different strategies: the Standard GA using a constant level of mutation, a mechanism called Random Immig ..."
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Cited by 92 (0 self)
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In this paper, we explore the use of alternative mutation strategies as a means of increasing diversity so that the GA can track the optimum of a changing environment. This paper contrasts three different strategies: the Standard GA using a constant level of mutation, a mechanism called Random Immigrants, that replaces part of the population each generation with randomly generated values, and an adaptive mechanism called Triggered Hypermutation, that increases the mutation rate whenever there is a degradation in the performance of the timeaveraged best performance. The study examines each of these strategies in the context of several kinds of environmental change, including linear translation of the optimum, random movement of the optimum, and oscillation between two significantly different landscapes. These first results should lead to the development of a single mechanism that can work well in both stationary and nonstationary environments. 1
An Overview of Genetic Algorithms: Part 1, Fundamentals
, 1993
"... this article may be reproduced for commercial purposes. 1 Introduction ..."
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Cited by 79 (1 self)
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this article may be reproduced for commercial purposes. 1 Introduction
Using Genetic Algorithms to Explore Pattern Recognition in the Immune System
, 1992
"... We describe an immune system model based on a universe of binary strings. The model is directed at understanding the pattern recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of our mod ..."
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Cited by 67 (6 self)
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We describe an immune system model based on a universe of binary strings. The model is directed at understanding the pattern recognition processes and learning that take place at both the individual and species levels in the immune system. The genetic algorithm (GA) is a central component of our model. In the paper we study the behavior of the GA on two pattern recognition problems that are relevant to natural immune systems. Finally, we compare our model with explicit fitness sharing techniques for genetic algorithms, and show that our model implements a form of implicit fitness sharing. 1 Introduction Our immune system protects us from an extraordinarily large variety of bacteria, viruses, and other pathogenic organisms. It also constantly surveys the body for the presence of abnormal cells, such as tumor cells and virally infected cells, and destroys such cells when they are found. To perform these tasks the immune system must be capable of distinguishing self cells and molecules, ...
The Evolution of Emergent Organization in Immune System Gene Libraries
 Proceedings of the 6th International Conference on Genetic Algorithms
, 1995
"... A binary model of the immune system is used to study the effects of evolution on the genetic encoding for antibody molecules. One feature of this encoding is that, unlike typical genetic algorithm experiments, not all genes found in the genotype are expressed in the phenotype. We report experiments ..."
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Cited by 42 (4 self)
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A binary model of the immune system is used to study the effects of evolution on the genetic encoding for antibody molecules. One feature of this encoding is that, unlike typical genetic algorithm experiments, not all genes found in the genotype are expressed in the phenotype. We report experiments which show that the evolution of immune system genes, simulated by the genetic algorithm, can induce a high degree of genetic organization even though that organization is not explicitly required by the fitness function. We hypothesize about the nature of this organization and introduce a measure called Hamming Separation to observe its change during the evolution of the immune system. 1 Introduction How can selection pressures operating only on the phenotype drive evolutionary changes in the genotype? In contrast with typical genetic algorithm representations, in which all genes contribute to the calculation of fitness, the genetic material in natural organisms is not completely expressed....
LibGA: A userfriendly workbench for orderbased genetic algorithm research
 Proceedings of the 1993 ACM/SIGAPP Symposium on Applied Computing
, 1993
"... Over the years there has been several packages developed that provide a workbench for genetic algorithm (GA) research. Most of these packages use the generational model inspired by GENESIS. A few have adopted the steadystate model used in Genitor. Unfortunately, they have some de ciencies when work ..."
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Cited by 36 (17 self)
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Over the years there has been several packages developed that provide a workbench for genetic algorithm (GA) research. Most of these packages use the generational model inspired by GENESIS. A few have adopted the steadystate model used in Genitor. Unfortunately, they have some de ciencies when working with orderbased problems such aspacking, routing, and scheduling. This paper describes LibGA, which was developed speci cally for orderbased problems, but which also works easily with other kinds of problems. It offers an easy to use `userfriendly ' interface and allows comparisons to be made between both generational and steadystate genetic algorithms for a particular problem. It includes a variety of genetic operators for reproduction, crossover, and mutation. LibGA makes it easy to use these operators in new ways for particular applications or to develop and include new operators. Finally, it o ers the unique new feature of a dynamic generation gap.
A Hybrid Approach to Modeling Metabolic Systems Using Genetic Algorithm and Simplex Method
, 1995
"... Genetic algorithms (GAs) have been shown to be a promising approach for a wide range of search and optimization problems. One of the main obstacles in applying GAs to complex problems has often been the high computational cost due to their slow convergence rate. We encountered such a difficulty in a ..."
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Cited by 32 (3 self)
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Genetic algorithms (GAs) have been shown to be a promising approach for a wide range of search and optimization problems. One of the main obstacles in applying GAs to complex problems has often been the high computational cost due to their slow convergence rate. We encountered such a difficulty in an attempt to use the classical GA for estimating parameters of a metabolic model. Adopting a common strategy in the literature for addressing the problem  integrating the GA with a complementary optimization technique, we developed a hybrid approach that combines a realcoded GA with a stochastic variant of simplex method in function optimization. Our empirical evaluations showed that the performance of our hybrid approach for the metabolic modeling problem improved those of a pure realcoded GA and an alternative simplexGA hybrid developed by Renders and Bersini. We showed that the hybrid approach also improved GA's convergence rate for a function optimization problem. Based on an empiric...
Multiobjective Optimization of Trusses using Genetic Algorithms
 COMPUTERS AND STRUCTURES
, 2000
"... In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of minmax optimum, a new GAbased multiobjective optimization technique is proposed and two truss design problems are solved using it. The results ..."
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Cited by 20 (0 self)
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In this paper we propose the use of the genetic algorithm (GA) as a tool to solve multiobjective optimization problems in structures. Using the concept of minmax optimum, a new GAbased multiobjective optimization technique is proposed and two truss design problems are solved using it. The results produced by this new approach are compared to those produced by other mathematical programming techniques and GAbased approaches, proving that this technique generates better tradeoffs and that the genetic algorithm can be used as a reliable numerical optimization tool.
An Evolutionary Heuristic for the Minimum Vertex Cover Problem
 KI94 Workshops (Extended Abstracts
, 1994
"... this paper, are used to compare the behavior of the genetic algorithm with the vercov heuristic. Recall that these graphs contain n = 3k + 4 (k 1) nodes distributed on three levels. They can be scaled up by choosing high values for k. We choose problem instances of the regular graph of sizes n = 10 ..."
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Cited by 16 (0 self)
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this paper, are used to compare the behavior of the genetic algorithm with the vercov heuristic. Recall that these graphs contain n = 3k + 4 (k 1) nodes distributed on three levels. They can be scaled up by choosing high values for k. We choose problem instances of the regular graph of sizes n = 100 (k = 32) and n = 202 (k = 66). For each of the problems a total of N = 100 independent runs of the vercov heuristic is performed, and the results are summarized in table 2. The same experiments were also performed for graphs of size n = 200 in order to test the behavior of the genetic algorithm as well as the vercov heuristic for an even larger problem size. In this case, the genetic algorithm was allowed to run for 4 \Delta 10