Sub-structural Niching in Estimation of Distribution Algorithms

We propose a sub-structural niching method that fully exploits the problem decomposition capability of linkage-learning methods such as the estimation of distribution algorithms and concentrate on maintaining diversity at the sub-structural level. The proposed method consists of three key components: (1) Problem decomposition and sub-structure identification, (2) sub-structure fitness estimation, and (3) sub-structural niche preservation. The sub-structural niching method is compared to restricted tournament selection (RTS)--a niching method used in hierarchical Bayesian optimization algorithm--with special emphasis on sustained preservation of multiple global solutions of a class of boundedly-difficult, additively-separable multimodal problems. The results show that sub-structural niching successfully maintains multiple global optima over large number of generations and does so with significantly less population than RTS. Additionally, the market share of each of the niche is much closer to the expected level in sub-structural niching when compared to RTS

Комбінований компетентний паралельний генетичний алгоритм та його застосування для задачі побудови розкладів

Розглянуто компетентні генетичні алгоритми, що навчаються зв’язності, а також способи паралельної реалізації генетичних алгоритмів. Запропоновано власний алгоритм, що об’єднує методики побудови генетичних алгоритмів, та репрезентовано спосіб розв’язання NP-повної задачі складання розкладу занять в університеті

КОМБІНОВАНИЙ КОМПЕТЕНТНИЙ ПАРАЛЕЛЬНИЙ ГЕНЕТИЧ-НИЙ АЛГОРИТМ ТА ЙОГО ЗАСТОСУВАННЯ ДЛЯ ЗАДАЧІ ПОБУ-ДОВИ РОЗКЛАДІВ

Розглянуто компетентні генетичні алгоритми, що навчаються зв’язності, а також способи паралельної реалізації генетичних алгоритмів. Запропоновано власний алгоритм, що об’єднує методики побудови генетичних алгоритмів, та репрезентовано спосіб розв’язання NP-повної задачі складання розкладу занять в університеті

Decomposition for Large-scale Optimization Problems with Overlapping Components

In this paper we use a divide-and-conquer approach to tackle large-scale optimization problems with overlapping components. Decomposition for an overlapping problem is challenging as its components depend on one another. The existing decomposition methods typically assign all the linked decision variables into one group, thus cannot reduce the original problem size. To address this issue we modify the Recursive Differential Grouping (RDG) method to decompose overlapping problems, by breaking the linkage at variables shared by multiple components. To evaluate the efficacy of our method, we extend two existing overlapping benchmark problems considering various level of overlap. Experimental results show that our method can greatly improve the search ability of an optimization algorithm via divide-and-conquer, and outperforms RDG, random decomposition as well as other state-of-the-art methods. We further evaluate our method using the CEC'2013 benchmark problems and show that our method is very competitive when equipped with a component optimizer

Cooperative co-evolution with differential grouping for large scale optimization

Cooperative co-evolution has been introduced into evolutionary algorithms with the aim of solving increasingly complex optimization problems through a divide-and-conquer paradigm. In theory, the idea of co-adapted subcomponents is desirable for solving large-scale optimization problems. However, in practice, without prior knowledge about the problem, it is not clear how the problem should be decomposed. In this paper, we propose an automatic decomposition strategy called differential grouping that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum. We show mathematically how such a decomposition strategy can be derived from a definition of partial separability. The empirical studies show that such near-optimal decomposition can greatly improve the solution quality on large-scale global optimization problems. Finally, we show how such an automated decomposition allows for a better approximation of the contribution of various subcomponents, leading to a more efficient assignment of the computational budget to various subcomponents

Linkage Identification by Non-monotonicity Detection for Overlapping Functions

This paper presents the linkage identification by non-monotonicity detection (LIMD) procedure and its extension for overlapping functions by introducing the tightness detection (TD) procedure. The LIMD identifies linkage groups directly by performing order-2 simultaneous perturbations on a pair of loci to detect monotonicity/non-monotonicity of fitness changes. The LIMD can identify linkage groups with at most order of k when it is applied to O(2k) strings. The TD procedure calculates tightness of linkage between a pair of loci based on the linkage groups obtained by the LIMD. By removing loci with weak tightness from linkage groups, correct linkage groups are obtained for overlapping functions, which were considered difficult for linkage identification procedures