Martin Pilát

Aggregate meta-models for evolutionary multiobjective and many-objective optimization

Abstract Evolutionary algorithms are among the best multiobjective optimizers. However, they need a large number of function evaluations. In this paper a meta-model based approach to the reduction in the needed number of function evaluations is presented. Local aggregate meta-models are used in a memetic operator. The algorithm is first discussed from a theoretical point of view and then it is shown that the meta-models greatly reduce the number of function evaluations. The approach is compared to a similar one with a single global meta-model as well as to more traditional NSGA-II and ϵ - IBEA . Moreover, it is shown that aggregate meta-models work even for a larger number of objectives and therefore should be considered when designing many-objective evolutionary algorithms.

ASM-MOMA: Multiobjective memetic algorithm with aggregate surrogate model

Evolutionary algorithms generally require a large number of objective function evaluations which can be costly in practice. These evaluations can be replaced by evaluations of a cheaper meta-model (surrogate model) of the objective functions. In this paper we present a novel distance based aggregate surrogate model for multiobjective optimization and describe a memetic multiobjective algorithm based on this model. Various variants of the models are tested and discussed and the algorithm is compared to standard multiobjective evolutionary algorithms. We show that our algorithm greatly reduces the number of required objective function evaluations.

Meta Learning in Multi-agent Systems for Data Mining

In this paper we present the Pikater multi-agent system designed for solving complex data mining tasks. We emphasize the unique intelligent features of the system -- its ability to search the parameter space of the data mining methods to find the optimal configuration, and meta learning -- finding the best possible method for the given data based on the ontological compatibility of datasets.

Hypervolume-based local search in multi-objective evolutionary optimization

This paper describes a surrogate based multi-objective evolutionary algorithm with hyper-volume contribution-based local search. The algorithm switches between an NSGA-II phase and a local search phase. In the local search phase, a model for each of the objectives is trained and CMA-ES is used to optimize the hyper-volume contribution of each individual with respect to its two neighbors on the non-dominated front. The performance of the algorithm is evaluated using the well known ZDT and WFG benchmark suites.

An Evolutionary Strategy for Surrogate-Based Multiobjective Optimization

The paper presents a surrogate-based evolutionary strategy for multiobjective optimization. The evolutionary strategy uses distance based aggregate surrogate models in two ways: as a part of memetic search and as way to pre-select individuals in order to avoid evaluation of bad individuals. The model predicts the distance of individuals to the currently known Pareto set. The newly proposed algorithm is compared to other algorithms which use similar surrogate models on a set of benchmark functions.

Incorporating User Preferences in MOEA/D through the Coevolution of Weights

The resulting set of solutions obtained by MOEA/D depends on the weights used in the decomposition. In this work, we use this feature to incorporate user preferences into the search. We use co-evolutionary approach to change the weights adaptively during the run of the algorithm. After the user specifies their preferences by assigning binary preference values to the individuals, the co-evolutionary step improves the distribution of weights by creating new (offspring) weights and selecting those that better match the user preferences. The algorithm is tested on a set of benchmark functions with a set of different user preferences.

Combining multiobjective and single-objective genetic algorithms in heterogeneous island model

The majority of multiobjective genetic algorithms is computationally expensive, therefore they often need to be parallelized before they can be used to solve practical tasks. Parallelization of multiobjective genetic algorithms is a relatively studied area, but no clearly winning approach has appeared yet. In this paper we present a novel parallel hybrid algorithm which combines multiobjective and single-objective genetic algorithms. We show that this algorithm can be successfully used to solve multiobjective optimization problems while outperforming more traditional parallel versions of multiobjective genetic algorithms.

LAMM-MMA: multiobjective memetic algorithm with local aggregate meta-model

In this paper we describe a multiobjective memetic algorithm utilizing local distance based meta-models. This algorithm is evaluated and compared to standard multiobjective evolutionary algorithms (MOEA) as well as to a similar algorithm with a global meta-model.

The effect of different local search algorithms on the performance of multi-objective optimizers

Several contemporary multi-objective surrogate-based algorithms use some kind of local search operator. The search technique used in this operator can largely affect the performance of the multi-objective optimizer as a whole, however, little attention is often paid to the selection of this technique. In this paper, we compare three different local search techniques and evaluate their effect on the performance of two different surrogate based multi-objective optimizers. The algorithms are evaluated using the well known ZDT and WFG benchmark suites and recommendations are made based on the results.

A Surrogate Based Multiobjective Evolution Strategy with Different Models for Local Search and Pre-selection

In this paper we present a multiobjective evolutionary algorithm which uses surrogate models in two different ways -- during a local search and during pre-selection. Two different approaches to surrogate modeling are used, and the algorithm provides multiple individuals in each generation to enable easy parallelization. The algorithm is tested and compared to standard multiobjective evolutionary algorithms and to our previously developed surrogate evolution strategy. We also discuss the importance of the use of two different approaches and show that it improves the convergence speed significantly.