This paper proposes a new approach by combining the Evolutionary Algorithm and Imperialist Competitive Algorithm. This approach tries to capture several people involved in community development characteristic. People live in different type of communities: Monarchy, Republic and Autocracy. People dominion is different in each community. Research work has been undertaken to deal with curse of dim...

This paper presents a hyper heuristic that is able to adapt two low level parameters (depth of search rate and mutation intensity) and the probability of applying a low level heuristic to an evolving solution in order to improve the solution quality. Basically, two random subsets of heuristics are maintained: one sub set of the full set of heuristics and other sub-set of the local heuristics. F...

In this paper we investigate the use of three evolutionary based heuristics to the open shop scheduling problem. The intractability of this problem is a motivation for the pursuit of heuristics that produce approximate solutions. This work introduces three evolutionary based heuristics, namely, a permutation genetic algorithm, a hybrid genetic algorithm and a sellsh gene algorithm, and tests th...

In this study a hybrid differential evolution-back-propagation algorithm to optimize the weights of feedforward neural network is proposed.The hybrid algorithm can achieve faster convergence speed with higher accuracy. The proposed hybrid algorithm combining differential evolution (DE) and back-propagation (BP) algorithm is referred to as DE-BP algorithm to train the weights of the feed-forward...

A data warehouse (DW) contains multiple views accessed by queries. One of the most important decisions in designing a DW is selecting views to materialize for the purpose of efficiently supporting decision making. The search space for possible materialized views is exponentially large. Therefore heuristics have been used to search for a near optimal solution. In this paper, we explore the use o...

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) and an improved non-dominating sorting multiobjective genetic algorithm (NSGA-II) are two well known multiobjective evolutionary algorithms (MOEAs) in the field of evolutionary computation. This paper mainly reviews their hybrid versions and some other algorithms which are developed for solving multiobjective optimization pro...

Deadline-constrained scheduling in project management is a NP-hard optimisation problem with major relevance in software engineering and other real-life situations dealing with the planning of activities that must be completed before specific dates. This article introduces efficient parallel versions for two evolutionary algorithms (genetic algorithm and hybrid evolutionary algorithm), to solve...

This research presents a novel approach to solve m-machine no-wait flowshop scheduling problem. A continuous flowshop problem with total flowtime as criterion is considered applying a hybrid evolutionary algorithm. The performance of the proposed method is evaluated and the results are compared with the best known in the literature. Experimental tests show the superiority of the evolutionary hy...