Honda Research Institute Europe Gmbh

Yaochu Jin

Bernhard Sendhoff

Markus Olhofer

Edward Tsang

Qingfu Zhang

Aimin Zhou

Tatsuya Okabe

A system and method for combining the model-based and genetics-based methods are combined according to a convergence criterion. When the population is not converged, the genetics-based approach is used, and when the population is converged, the model-based method is used to generate offspring. The algorithm benefits from using a model-based offspring generation only when the population shows a certain degree of regularity, i.e., converged in a stochastic sense. In addition, a more sophisticated method to construct the stochastic part of the model can be used. Also a biased Gaussian noise (the mean of the noise is not zero), as well as a white Gaussian noise (the mean of the noise is zero) can be preferably used for the stochastic part of the model.

Combining model-based and genetics-based offspring generation for multi-objective optimization using a convergence criterion

The underlying invention generally relates to the field of Estimation of Distribution Algorithm, especially to optimization problems, including single-objective optimization and Multi-Objective Optimization. The proposed method for optimization comprises six steps. In a first step it provides an initial population or a data set with a plurality of members respectively represented by parameter sets. Then one or a plurality of fitness functions are applied to evaluate the quality of the members of the population. In a third step offspring of the population is generated by means of a stochastic model using information from all members of the population. One or a plurality of fitness functions are applied to evaluate the quality of the offspring with respect to the underlying problem of the optimization. In a fifth step offspring is selected. Lastly the method goes back to the third step until the quality reaches a threshold value.

System and method for estimation of a distribution algorithm

A method to obtain the Pareto solutions that are specified by human preferences is suggested. The main idea is to convert the fuzzy preferences into interval-based weights. With the help of the dynamically-weighted aggregation method, it is shown to be successful to find the preferred solutions on two test functions with a convex Pareto front. Compared to the method described in 'Use of Preferences for GA-based Multi-Objective Optimization' (Proceedings of 1999 Genetic and Evolutionary Computation Conference, pp. 1504-1510, 1999) by Cvetkovic et al., the method according to the invention is able to find a number of solutions instead of only one, given a set of fuzzy preferences over different objectives. This is consistent with the motivation of fuzzy logic.

Fuzzy preferences in multi-objective optimization (MOO)

In the field of multi-objective optimization using evolutionary algorithms conventionally different objectives are aggregated and combined into one objective function using a fixed weight when more than one objective needs to be optimized. With such a weighted aggregation, only one solution can be obtained in one run. Therefore, according to the present invention two methods to change the weights systematically and dynamically during the evolutionary optimization are proposed. One method is to assign uniformly distributed weight to each individual in the population of the evolutionary algorithm. The other method is to change the weight periodically when the evolution proceeds. In this way a full set of Pareto solutions can be obtained in one single run.

Methods for multi-objective optimization using evolutionary algorithms

The invention relates to an evolutionary optimization method. First, an initial population of individuals is set up and an original fitness function is applied. Then the offspring individuals having a high evaluated quality value as parents are selected. In a third step, the parents are reproduced to create a plurality of offspring individuals. The quality of the offspring individuals is evaluated selectively using an original fitness function or an approximate fitness function. Finally, the method returns to the selection step until a termination condition is met. The step of evaluating the quality of the offspring individuals includes grouping all offspring individuals in clusters, selecting for each cluster one or a plurality of offspring individuals, resulting in altogether selected offspring individuals, evaluating the selected offspring individuals by the original fitness function, and evaluating the remaining offspring individuals by means of the approximate fitness function.

Reduction of fitness evaluations using clustering techniques and neural network ensembles

A framework for managing approximate models in generation-based evolution control is proposed. This framework is well suited for parallel evolutionary optimization that is able to guarantee the correct convergence of the evolutionary algorithm and to reduce the computation costs as much as possible. Control of the evolution and updating of the approximate models are based on the estimated fidelity of the approximate model. The frequency at which the original function is called and the approximate model is updated is determined by the local fidelity of the approximate model. By local fidelity the fidelity of the model for the region where the current population is located is designated. The lower the model fidelity is, the more frequently the original function should be called and the approximate models should be updated.

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Rodgau, Germany

Yaochu Jin