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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 210 (18 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...
Niching Methods for Genetic Algorithms
, 1995
"... Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This ..."
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Cited by 191 (1 self)
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Niching methods extend genetic algorithms to domains that require the location and maintenance of multiple solutions. Such domains include classification and machine learning, multimodal function optimization, multiobjective function optimization, and simulation of complex and adaptive systems. This study presents a comprehensive treatment of niching methods and the related topic of population diversity. Its purpose is to analyze existing niching methods and to design improved niching methods. To achieve this purpose, it first develops a general framework for the modelling of niching methods, and then applies this framework to construct models of individual niching methods, specifically crowding and sharing methods. Using a constructed model of crowding, this study determines why crowding methods over the last two decades have not made effective niching methods. A series of tests and design modifications results in the development of a highly effective form of crowding, called determin...
Multiobjective Optimization Using the Niched Pareto Genetic Algorithm
, 1993
"... Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives (i.e., attributes or criteria) have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives ..."
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Cited by 111 (4 self)
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Many, if not most, optimization problems have multiple objectives. Historically, multiple objectives (i.e., attributes or criteria) have been combined ad hoc to form a scalar objective function, usually through a linear combination (weighted sum) of the multiple attributes, or by turning objectives into constraints. The most recent development in the field of decision analysis has yielded a rigorous technique for combining attributes multiplicatively (thereby incorporating nonlinearity), and for handling uncertainty in the attribute values. But MultiAttribute Utility Analysis (MAUA) provides only a mapping from a vectorvalued objective function to a scalarvalued function, and does not address the difficulty of searching large problem spaces. Genetic algorithms (GAs), on the other hand, are well suited to searching intractably large, poorly understood problem spaces, but have mostly been used to optimize a single objective. The direct combination of MAUA and GAs is a logical next step...
Finding Multimodal Solutions Using Restricted Tournament Selection
 Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... This paper investigates a new technique for the solving of multimodal problems using genetic algorithms (GAs). The proposed technique, Restricted Tournament Selection, is based on the paradigm of local competition. The paper begins by discussing some of the drawbacks of using current multimodal tech ..."
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Cited by 104 (1 self)
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This paper investigates a new technique for the solving of multimodal problems using genetic algorithms (GAs). The proposed technique, Restricted Tournament Selection, is based on the paradigm of local competition. The paper begins by discussing some of the drawbacks of using current multimodal techniques. The paper then presents the new technique along with an analysis of a class of sets of solutions it preserves and locates. This presentation researches the new technique's restriction on competition from the viewpoint of calculating probability distributions for its tournaments as well as its various niche takeover times. Empirical observations are then presented as evidence of the technique's abilities in a wide variety of settings. Finally, this paper explores the future trajectory of multimodal GA research. Finding Multimodal Solutions Using Restricted Tournament Selection Abstract This paper investigates a new technique for the solving of multimodal problems using genetic algor...
Escaping Hierarchical Traps with Competent Genetic Algorithms
 Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2001
, 2001
"... To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. We propose the hierarchical ... ..."
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Cited by 85 (46 self)
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To solve hierarchical problems, one must be able to learn the linkage, represent partial solutions efficiently, and assure effective niching. We propose the hierarchical ...
SearchIntensive Concept Induction
, 1995
"... This paper describes REGAL, a distributed genetic algorithmbased system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptio ..."
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Cited by 77 (3 self)
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This paper describes REGAL, a distributed genetic algorithmbased system, designed for learning First Order Logic concept descriptions from examples. The system is a hybrid between the Pittsburgh and the Michigan approaches, as the population constitutes a redundant set of partial concept descriptions, each evolved separately. In order to increase effectiveness, REGAL is specifically tailored to the concept learning task; hence, REGAL is taskdependent, but, on the other hand, domainindependent. The system proved to be particularly robust with respect to parameter setting across a variety of different application domains. REGAL is based on a selection operator, called Universal Suffrage operator, provably allowing the population to asymptotically converge, in average, to an equilibrium state, in which several species coexist. The system is presented both in a serial and in a parallel version, and a new distributed computational model is proposed and discussed. The system has been test...
Parallel Recombinative Simulated Annealing: A Genetic Algorithm
, 1995
"... This paper introduces and analyzes a parallel method of simulated annealing. Borrowing from genetic algorithms, an effective combination of simulated annealing and genetic algorithms, called parallel recombinative simulated annealing, is developed. This new algorithm strives to retain the desirable ..."
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Cited by 74 (3 self)
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This paper introduces and analyzes a parallel method of simulated annealing. Borrowing from genetic algorithms, an effective combination of simulated annealing and genetic algorithms, called parallel recombinative simulated annealing, is developed. This new algorithm strives to retain the desirable asymptotic convergence properties of simulated annealing, while adding the populations approach and recombinative power of genetic algorithms. The algorithm iterates a population of solutions rather than a single solution, employing a binary recombination operator as well as a unary neighborhood operator. Proofs of global convergence are given for two variations of the algorithm. Convergence behavior is examined, and empirical distributions are compared to Boltzmann distributions. Parallel recombinative simulated annealing is amenable to straightforward implementation on SIMD, MIMD, or sharedmemory machines. The algorithm, implemented on the CM5, is run repeatedly on two deceptive problems...
A Comparison of Parallel and Sequential Niching Methods
 In Proceedings of the Sixth International Conference on Genetic Algorithms
, 1995
"... Niching methods extend genetic algorithms to domains that require the location of multiple solutions. This study examines and compares four niching methods  sharing, crowding, sequential niching, and parallel hillclimbing. It focuses on the differences between parallel and sequential niching. Th ..."
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Cited by 68 (0 self)
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Niching methods extend genetic algorithms to domains that require the location of multiple solutions. This study examines and compares four niching methods  sharing, crowding, sequential niching, and parallel hillclimbing. It focuses on the differences between parallel and sequential niching. The niching methods undergo rigorous testing on optimization and classification problems of increasing difficulty. A nichingbased technique is introduced that extends genetic algorithms to classification problems. 1 INTRODUCTION Niching methods (Mahfoud, 1995) promote the formation and maintenance of stable subpopulations in genetic algorithms (GAs), allowing GAs to extend their problemsolving power to complex domains. This study examines four niching methods and compares their performances on problems in both classification and multimodal function optimization. The problems cover a wide range of difficulty levels. Classification problems are solved via a new nichingbased technique. Paral...
Implicit Niching in a Learning Classifier System: Nature's Way
 EVOLUTIONARY COMPUTATION
, 1994
"... We approach the difficult task of analyzing the complex behavior of even the simplest learning classifier system (LCS) by isolating one crucial subfunction in the LCS learning algorithm: covering through niching. The LCS must maintain a population of diverse rules that together solve a problem (e.g. ..."
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Cited by 56 (9 self)
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We approach the difficult task of analyzing the complex behavior of even the simplest learning classifier system (LCS) by isolating one crucial subfunction in the LCS learning algorithm: covering through niching. The LCS must maintain a population of diverse rules that together solve a problem (e.g., classify examples). To maintain a diverse population while applying the GA's selection operator, the LCS must incorporate some kind of niching mechanism. The natural way to accomplish niching in an LCS is to force competing rules to share resources (i.e., rewards). This implicit LCS fitness sharing is similar to the explicit fitness sharing used in many niched GAs. Indeed, the LCS implicit sharing algorithm can be mapped onto explicit fitness sharing with a onetoone correspondence between algorithm components. This mapping is important because several studies of explicit fitness sharing, and of niching in GAs generally, have produced key insights and analytical tools for understanding th...
Simple Subpopulation Schemes +
 In
, 1994
"... This paper considers a new method for maintaining diversity by creating subpopulations in a standard generational evolutionary algorithm. Unlike other methods, it replaces the concept of distance between individuals with tag bits that identify the subpopulation to which an individual belongs. Two va ..."
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Cited by 52 (2 self)
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This paper considers a new method for maintaining diversity by creating subpopulations in a standard generational evolutionary algorithm. Unlike other methods, it replaces the concept of distance between individuals with tag bits that identify the subpopulation to which an individual belongs. Two variations of this method are presented, illustrating the feasibility of this approach. 1. Introduction One of the attractive features of an evolutionary algorithm (EA) is that it quickly concentrates effort in promising areas of a search space. Unfortunately, this feature is not always advantageous for many applications of interest. In such cases the attractive feature is described in a negative tone as "premature convergence". Although this phrase is subjective, it refers to the phenomenon in which the EA loses population diversity before some goal is met. Two typical cases are the optimization of multimodal functions and covering problems. In the former case the EA may concentrate effort o...