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Genetic Programming
, 1997
"... Introduction Genetic programming is a domainindependent problemsolving approach in which computer programs are evolved to solve, or approximately solve, problems. Genetic programming is based on the Darwinian principle of reproduction and survival of the fittest and analogs of naturally occurring ..."
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Cited by 1056 (12 self)
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is now called the genetic algorithm (GA). The genetic algorithm attempts to find a good (or best) solution to the problem by genetically breeding a population of individuals over a series of generations. In the genetic algorithm, each individual in the population represents a candidate solut
A Fast Elitist NonDominated Sorting Genetic Algorithm for MultiObjective Optimization: NSGAII
, 2000
"... Multiobjective evolutionary algorithms which use nondominated sorting and sharing have been mainly criticized for their (i) 4 computational complexity (where is the number of objectives and is the population size), (ii) nonelitism approach, and (iii) the need for specifying a sharing ..."
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Cited by 662 (15 self)
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sharing parameter. In this paper, we suggest a nondominated sorting based multiobjective evolutionary algorithm (we called it the Nondominated Sorting GAII or NSGAII) which alleviates all the above three difficulties. Specifically, a fast nondominated sorting approach with computational
Multiobjective Optimization Using Nondominated Sorting in Genetic Algorithms
 Evolutionary Computation
, 1994
"... In trying to solve multiobjective optimization problems, many traditional methods scalarize the objective vector into a single objective. In those cases, the obtained solution is highly sensitive to the weight vector used in the scalarization process and demands the user to have knowledge about t ..."
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Cited by 539 (5 self)
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number of solutions simultaneously. Although a vector evaluated GA (VEGA) has been implemented by Schaffer and has been tried to solve a number of multiobjective problems, the algorithm seems to have bias towards some regions. In this paper, we investigate Goldberg's notion of nondominated
A Fast and Elitist MultiObjective Genetic Algorithm: NSGAII
, 2000
"... Multiobjective evolutionary algorithms which use nondominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) nonelitism approach, and (iii) the need for specifying a sharing param ..."
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Cited by 1815 (60 self)
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parameter. In this paper, we suggest a nondominated sorting based multiobjective evolutionary algorithm (we called it the Nondominated Sorting GAII or NSGAII) which alleviates all the above three difficulties. Specifically, a fast nondominated sorting approach with O(MN ) computational complexity
Toward a GA Solution to the Discovery Problem
, 1992
"... Classifier systems are an intriguing idea that, so far, has been hard to make work. The difficulties fall into three areas that I call the Discovery Problem, the Cooperation Problem, and the Time Problem. Here I will address the Discovery Problem, but the others deserve some mention. The Cooperatio ..."
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. The Cooperation Problem arises because in a classifier system, classifiers must often cooperate and yet are also in competition under the GA. Classifiers may cooperate diachronically, forming chains along which payoff flows. Each member of a chain is dependent on the others: the early on the late and vice
Genetic Algorithms, Noise, and the Sizing of Populations
 COMPLEX SYSTEMS
, 1991
"... This paper considers the effect of stochasticity on the quality of convergence of genetic algorithms (GAs). In many problems, the variance of buildingblock fitness or socalled collateral noise is the major source of variance, and a populationsizing equation is derived to ensure that average sig ..."
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Cited by 276 (85 self)
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equation may be viewed as a coarse delineation of a boundary between what a physicist might call two distinct phases of GA behavior. At low population sizes the GA makes many errors of decision, and the quality of convergence is largely left to the vagaries of chance or the serial fixup of flawed
The Gambler's Ruin Problem, Genetic Algorithms, and the Sizing of Populations
, 1997
"... This paper presents a model for predicting the convergence quality of genetic algorithms. The model incorporates previous knowledge about decision making in genetic algorithms and the initial supply of building blocks in a novel way. The result is an equation that accurately predicts the quality of ..."
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Cited by 245 (89 self)
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of the solution found by a GA using a given population size. Adjustments for different selection intensities are considered and computational experiments demonstrate the effectiveness of the model. I. Introduction The size of the population in a genetic algorithm (GA) is a major factor in determining the quality
International Conference on Innovations In Intelligent Instrumentation, Optimization And Signal Processing “ICIIIOSP2013” 5 A Hybrid ABCGA Solution for the Economic Dispatch of Generation in Power System
"... The power system optimization is one of the most important aspects of power engineering with respect to cost. The running cost of the thermal power plant is high. The main objective of this paper is to minimize the total fuel cost of the thermal power plant. Economic dispatch is the optimal allocati ..."
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the overall search capability of two powerful heuristic optimization algorithms: Artificial Bee colony (ABC) and Genetic Algorithm (GA). Here, the minimum fuel cost of thermal power plant with six generators is determined using a hybrid ABCGA technique and is proved to give less cost than GA and ABC
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 129 (1 self)
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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
On the Use of NonStationary Penalty Functions to Solve Nonlinear Constrained Optimization Problems with GA's
 In
, 1994
"... In this paper we discuss the use of nonstationary penalty functions to solve general nonlinear programming problems (NP ) using realvalued GAs. The nonstationary penalty is a function of the generation number; as the number of generations increases so does the penalty. Therefore, as the penalty i ..."
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Cited by 139 (7 self)
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increases it puts more and more selective pressure on the GA to find a feasible solution. The ideas presented in this paper come from two basic areas: calculusbased nonlinear programming and simulated annealing. The nonstationary penalty methods are tested on four NP test cases and the effectiveness
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