Different types of evolutionary algorithms have been developed for constrained continuous optimization. We carry out a feature-based analysis of evolved constrained continuous optimization instances to understand the characteristics of constraints that make problems hard for evolutionary algorithm. In our study, we examine how various sets of constraints can influence the behaviour of ε-Constra...

We present a study on the difficulty of solving binary constraint satisfaction problems where an evolutionary algorithm is used to explore the space of problem instances. By directly altering the structure of problem instances and by evaluating the effort it takes to solve them using a complete algorithm we show that the evolutionary algorithm is able to detect problem instances that are harder...

Abstract: The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has received attention from researchers in recent years. This paper presents a new multiobjective algorithm based on decomposition and the cloud model called multiobjective decomposition evolutionary algorithm based on Cloud Particle Differential Evolution (MOEA/D-CPDE). In the proposed method, the best solution...

A highly competitive micro evolutionary algorithm to solve unconstrained optimization problems called μJADE (micro adaptive differential evolution), is adapted to deal with constrained search spaces. Two constraint-handling techniques (the feasibility rules and the ε-constrained method) are tested in μJADE and their performance is analyzed. The most competitive version is then compared against ...

In recent years a new evolutionary algorithm for optimization in continuos spaces called Differential Evolution (DE) has developed. DE turns out to need only few evaluation steps to minimize a function. This makes it an interesting candidate for problem domains with high computational costs as for instance in the automatic generation of programs. In this paper a DE-based tree discovering algori...

Binary tournament (BT) selection is known as an established operator that has been employed in various problems. However, the development of evolutionary algorithms (EA), this a drawback providing efficient implementation union procedure, which cannot guarantee parsimonious knowledge base with reduced number rules. Therefore, paper introduces binary-standard deviation (SD) into EA enhancement B...

Differential Evolution algorithm is a new competitive heuristic optimization algorithm in the continuous field. The operators in the original Differential Evolution are simple; however, these operators make it impossible to use the Differential Evolution in the binary space directly. Based on the analysis of problems led by the mutation operator of the original Differential Evolution in the bin...

Predicting the amount of sediment in water resource projects is one most important measures to be taken, while sediments have an unknown nature their behavior. In this research, using data recorded at Mazrae station between 2002 and 2013, catchment area Maku Dam has been predicted different models intelligent algorithms. Recorded including river flow (m 3 /s), concentration (mg/L), temperature ...

In the current paper a rigorous mathematical language for comparing evolutionary computation techniques via their representation is developed. A binary semi-genetic algorithm is introduced, and it is proved that in a certain sense any reasonable evolutionary search algorithm can be re-encoded by a binary semi-genetic algorithm (see corollaries 15 and 16). Moreover, an explicit bijection between...