ICHEA - A Constraint Guided Search for Improving Evolutionary Algorithms

Many science and engineering applications require finding solutions to optimization problems by satisfying a set of constraints. These problems are typically NP-complete and can be formalized as constraint satisfaction problems (CSPs). Evolutionary algorithms (EAs) are good solvers for optimization problems ubiquitous in various problem domains. EAs have also been used to solve CSPs, however traditional EAs are ‘blind’ to constraints as they do not exploit information from the constraints in search for solutions. In this paper, a variation of EA is proposed where information is extracted from the constraints and exploited in search. The proposed model (ICHEA for Intelligent Constraint Handling Evolutionary Algorithm) improves on efficiency and is independent of problem characteristics. This paper presents ICHEA and its results from solving continuous CSPs. The results are significantly better than results from other existing approaches and the model shows strong potential. The scope is to finding at least one solution that satisfies all the constraints rather than optimizing the solutions.