Results 1  10
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343
Biogeographybased optimization,”
 IEEE Transactions on Evolutionary Computation,
, 2008
"... AbstractWe propose a novel variation to biogeographybased optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs oppositionbased learning (OBL) alongside BBO's migration rates to create oppositional BBO (O BBO). Additionally, ..."
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Cited by 136 (31 self)
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AbstractWe propose a novel variation to biogeographybased optimization (BBO), which is an evolutionary algorithm (EA) developed for global optimization. The new algorithm employs oppositionbased learning (OBL) alongside BBO's migration rates to create oppositional BBO (O BBO). Additionally, a new opposition method named quasireflection is introduced. Quasireflection is based on opposite numbers theory and we mathematically prove that it has the highest expected probability of being closer to the problem solution among all OBL methods. The oppositional algorithm is further revised by the addition of dynamic domain scaling and weighted reflection. Simulations have been performed to validate the performance of quasiopposition as well as a mathematical analysis for a singledimensional problem. Empirical results demonstrate that with the assistance of quasireflection, OB BO significantly outperforms BBO in terms of success rate and the number of fitness function evaluations required to find an optimal solution. Index TermsBiogeographybased optimization (BBO), evolutionary algorithms, oppositionbased learning, opposite numbers, quasiopposite numbers, quasireflected numbers, probability.
Differential evolution algorithm with strategy adaptation for global numerical optimization
 IEEE Trans. Evol. Comput
, 2009
"... Abstract—Differential evolution (DE) is an efficient and powerful populationbased stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem c ..."
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Cited by 125 (9 self)
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Abstract—Differential evolution (DE) is an efficient and powerful populationbased stochastic search technique for solving optimization problems over continuous space, which has been widely applied in many scientific and engineering fields. However, the success of DE in solving a specific problem crucially depends on appropriately choosing trial vector generation strategies and their associated control parameter values. Employing a trialanderror scheme to search for the most suitable strategy and its associated parameter settings requires high computational costs. Moreover, at different stages of evolution, different strategies coupled with different parameter settings may be required in order to achieve the best performance. In this paper, we propose a selfadaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually selfadapted by learning from their previous experiences in generating promising solutions. Consequently, a more suitable generation strategy along with its parameter settings can be determined adaptively to match different phases of the search process/evolution. The performance of the SaDE algorithm is extensively evaluated (using codes available from P. N. Suganthan) on a suite of 26 boundconstrained numerical optimization problems and compares favorably with the conventional DE and several stateoftheart parameter adaptive DE variants. Index Terms—Differential evolution (DE), global numerical optimization, parameter adaptation, selfadaptation, strategy adaptation. I.
Quantuminspired Evolutionary Algorithm for a Class of Combinatorial Optimization
 IEEE TRANS. EVOLUTIONARY COMPUTATION
, 2002
"... This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantuminspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is a ..."
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Cited by 112 (7 self)
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This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantuminspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, the evaluation function, and the population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Qbit, defined as the smallest unit of information, for the probabilistic representation and a Qbit individual as a string of Qbits. A Qgate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments are carried out on the knapsack problem, which is a wellknown combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.
Adaptive Particle Swarm Optimization
, 2008
"... This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an ‘evolutionary factor’ by using the population distribution information and relative ..."
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Cited by 67 (2 self)
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This paper proposes an adaptive particle swarm optimization (APSO) with adaptive parameters and elitist learning strategy (ELS) based on the evolutionary state estimation (ESE) approach. The ESE approach develops an ‘evolutionary factor’ by using the population distribution information and relative particle fitness information in each generation, and estimates the evolutionary state through a fuzzy classification method. According to the identified state and taking into account various effects of the algorithmcontrolling parameters, adaptive control strategies are developed for the inertia weight and acceleration coefficients for faster convergence speed. Further, an adaptive ‘elitist learning strategy ’ (ELS) is designed for the best particle to jump out of possible local optima and/or to refine its accuracy, resulting in substantially improved quality of global solutions. The APSO algorithm is tested on 6 unimodal and multimodal functions, and the experimental results demonstrate that the APSO generally outperforms the compared PSOs, in terms of solution accuracy, convergence speed and algorithm reliability.
Evolutionary Programming Using Mutations Based on the Lévy Probability Distribution
, 2004
"... This paper studies evolutionary programming with mutations based on the Lvy probability distribution. The Lvy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutati ..."
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Cited by 65 (9 self)
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This paper studies evolutionary programming with mutations based on the Lvy probability distribution. The Lvy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter in the Lvy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Lvy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.
Large Scale Evolutionary Optimization Using Cooperative Coevolution
, 2007
"... Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectiveness and advantages when applied to large and complex problems, e.g., those with high dimensions. Although cooperative coevo ..."
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Cited by 64 (18 self)
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Evolutionary algorithms (EAs) have been applied with success to many numerical and combinatorial optimization problems in recent years. However, they often lose their effectiveness and advantages when applied to large and complex problems, e.g., those with high dimensions. Although cooperative coevolution has been proposed as a promising framework for tackling highdimensional optimization problems, only limited studies were reported by decomposing a highdimensional problem into single variables (dimensions). Such methods of decomposition often failed to solve nonseparable problems, for which tight interactions exist among different decision variables. In this paper, we propose a new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems. A random grouping scheme and adaptive weighting are introduced in problem decomposition and coevolution. Instead of conventional evolutionary algorithms, a novel differential evolution algorithm is adopted. Theoretical analysis is presented in this paper to show why and how the new framework can be effective for optimizing large nonseparable problems. Extensive computational studies are also carried out to evaluate the performance of newly proposed algorithm on a large number of benchmark functions with up to 1000 dimensions. The results show clearly that our framework and algorithm are effective as well as efficient for large scale evolutionary optimisation problems. We are unaware of any other evolutionary algorithms that can optimize 1000dimension nonseparable problems as effectively and efficiently as we have done.
TimeSeries Forecasting Using Flexible Neural Tree Model
, 2004
"... Timeseries forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the timeseries forecasting models. This paper introduces a new timeseries forecasting model based on the flexible neural tree (FNT). The FNT mode ..."
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Cited by 54 (21 self)
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Timeseries forecasting is an important research and application area. Much effort has been devoted over the past several decades to develop and improve the timeseries forecasting models. This paper introduces a new timeseries forecasting model based on the flexible neural tree (FNT). The FNT model is generated initially as a flexible multilayer feedforward neural network and evolved using an evolutionary procedure. Very often it is a difficult task to select the proper input variables or timelags for constructing a timeseries model. Our research demonstrates that the FNT model is capable of handing the task automatically. The performance and effectiveness of the proposed method are evaluated using time series prediction problems and compared with those of related methods.
Fast evolutionary programming
 Proceeding on Fifth Annual Conference on Evolutionary Programming
, 1996
"... AbstmctThis paper presents a study of parallel evolutionary programming (EP). The paper is divided into two parts. The first part proposes a concept of parallel EP. Four numerical fmctions are used to compare the performance between the serial algorithm and the parallel algorithm. In the second par ..."
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Cited by 51 (4 self)
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AbstmctThis paper presents a study of parallel evolutionary programming (EP). The paper is divided into two parts. The first part proposes a concept of parallel EP. Four numerical fmctions are used to compare the performance between the serial algorithm and the parallel algorithm. In the second part, we apply parallel Ep to a more complicated problem an evolving neural networks pmhlem. The results from this problem show that the parallel version h not only faster than the serial version, but the parallel version also more reliably finds optimal solutions. I.
Differential Evolution Using a NeighborhoodBased Mutation Operator
, 2009
"... Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and re ..."
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Cited by 42 (8 self)
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Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. It has reportedly outperformed a few evolutionary algorithms (EAs) and other search heuristics like the particle swarm optimization (PSO) when tested over both benchmark and realworld problems. DE, however, is not completely free from the problems of slow and/or premature convergence. This paper describes a family of improved variants of the DE/targettobest/1/bin scheme, which utilizes the concept of the neighborhood of each population member. The idea of small neighborhoods, defined over the indexgraph of parameter vectors, draws inspiration from the community of the PSO algorithms. The proposed schemes balance the exploration and exploitation abilities of DE without imposing serious additional burdens in terms of function evaluations. They are shown to be statistically significantly better than or at least comparable to several existing DE variants as well as a few other significant evolutionary computing techniques over a test suite of 24 benchmark functions. The paper also investigates the applications of the new DE variants to two reallife problems concerning parameter estimation for frequency modulated sound waves and spread spectrum radar polyphase code design.
Two improved differential evolution schemes for faster global search
 in Proc. ACMSIGEVO GECCO
, 2005
"... Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. In this paper we present two new, improved variants of DE. Performance comparisons of the two proposed methods are provided against (a) the original DE, (b) the canonical partic ..."
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Cited by 39 (9 self)
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Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. In this paper we present two new, improved variants of DE. Performance comparisons of the two proposed methods are provided against (a) the original DE, (b) the canonical particle swarm optimization (PSO), and (c) two PSOvariants. The new DEvariants are shown to be statistically significantly better on a sevenfunction test bed for the following performance measures: solution quality, time to find the solution, frequency of finding the solution, and scalability. Categories and Subject Descriptors