Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation

Sano, Yasuhito; Kita, Hidetoshi

doi:10.1109/cec.2002.1006261

Cited by 53 publications

(25 citation statements)

References 6 publications

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“…In this study, noise is implemented as an additive normal distributed perturbation with zero mean. It is assumed that noise has a disruptive influence on the value of each individual in the objective space [3], [6], [19], [20], [33], i.e., Table 1 are modified in the form of Eqn. (1) in order to include the influence of noise.…”

Section: B Noisy Objective Functionsmentioning

confidence: 99%

“…According to Beyer [4], noise reduces EA's convergence rate and causes convergence to a sub-optimal solution. Researchers have sought to reduce the detrimental effects of noise by means of appropriate population sizing [17], [30], fitness averaging and fitness estimation [6], [33] and specific selection mechanism [32], [2]. There also exist more complex methods.…”

Section: Introductionmentioning

confidence: 99%

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An Investigation on Noisy Environments in Evolutionary Multi-Objective Optimization

Goh

Chiam

Tan

2006

2006 IEEE Conference on Cybernetics and Intelligent Systems

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In addition to the need of satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. While studies have shown that many multi-objective evolutionary optimizers are capable of achieving optimization goals, their ability to deal with noise is rarely studied. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multi-objective optimization based upon five benchmark problems characterized by different difficulties in local optimality, non-uniformity, discontinuity and non-convexity. Interestingly, the baseline algorithm employed tends to evolve better solution sets in the presence of low noise levels for some problems. Nevertheless, the evolutionary optimization process degenerates into random search under increasing noise levels.

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Section: B Noisy Objective Functionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Investigation on Noisy Environments in Evolutionary Multi-Objective Optimization

Goh

Chiam

Tan

2006

2006 IEEE Conference on Cybernetics and Intelligent Systems

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“…It has also been concluded that thresholding requires a modified adaptation rule for the mutation rate. Kita and Sano [12] introduced a memorybased fitness evaluation GA (MFEGA). The main idea of MFEGA is to store the sampled fitness values into the memory as a search history, and then estimate the fitness values for points of interest using the history array.…”

Section: Introductionmentioning

confidence: 99%

Investigation of a changing range genetic algorithm in noisy environments

Amirjanov

2007

Numerical Meth Engineering

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SUMMARYThis paper analyses the effect of noise on the performance of a changing range genetic algorithm (CRGA). CRGA adaptively shifts and shrinks the size of the search space of the feasible region by employing feasible and infeasible solutions in the population to reach the global optimum. An additional modification of CRGA was introduced to reduce the effects of noise on the performance of the algorithm. Several test cases demonstrated the ability of the improved CRGA to deal with an additive noise.

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“…Takahashi et al [58] use fitness estimation from a statistical model of the history of solutions to deal with noisy fitness functions; the example given is on a weight vector with discrete values. This builds on the earlier work with continuous functions in [42]. Finally, in [34] the author builds a surrogate from a Gibbs model which is derived from the distribution learnt by an EDA.…”

mentioning

confidence: 99%

“…A model may be used where no explicit fitness function exists such as in evolutionary art and music [22]. Further, a fitness model may be employed to simplify the search by reducing noise [42,58,10] or smoothing a multimodal landscape [63].…”

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confidence: 99%

The Markov Network Fitness Model

Brownlee

McCall

Shakya

2012

Adaptation, Learning, and Optimization

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Fitness modelling is an area of research which has recently received much interest among the evolutionary computing community. Fitness models can improve the efficiency of optimisation through direct sampling to generate new solutions, guiding of traditional genetic operators or as surrogates for a noisy or long-running fitness functions. In this chapter we discuss the application of Markov networks to fitness modelling of black-box functions within evolutionary computation, accompanied by discussion on the relationship between Markov networks and Walsh analysis of fitness functions. We review alternative fitness modelling and approximation techniques and draw comparisons with the Markov network approach. We discuss the applicability of Markov networks as fitness surrogates which may be used for constructing guided operators or more general hybrid algorithms. We conclude with some observations and issues which arise from work conducted in this area so far.

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Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation

Cited by 53 publications

References 6 publications

An Investigation on Noisy Environments in Evolutionary Multi-Objective Optimization

An Investigation on Noisy Environments in Evolutionary Multi-Objective Optimization

Investigation of a changing range genetic algorithm in noisy environments

The Markov Network Fitness Model

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