Nowadays, large scale optimisation problems arise as a very interesting field of research, because they appear in many real-world problems (bio-computing, data mining, etc.). Thus, scalability becomes an essential requirement for modern optimisation algorithms. In a previous work, we presented memetic algorithms based on local search chains. Local search chain concerns the idea that, at one sta...

This paper presents and examines the behaviour of a system whereby the rules governing local search within a Memetic Algorithm are co-evolved alongside the problem representation. We describe the rationale for such a system, and then describe the implementation of a simple version in which the evolving rules are encoded as (condition:action) patterns applied to the problem representation. We in...

A memetic algorithm for tackling multiobjective optimization problems is presented. The algorithm employs the proven local search strategy used in the Pareto archived evolution strategy (PAES) and combines it with the use of a population and recombination. Verification of the new algorithm is carried out by testing it on a set of multiobjective 0/1 knapsack problems. On each problem instance, c...

Course Timetabling is a complex problem, happening at the beginning of every semester at universities. In this problem, one of the most important issues is variety of constraints, which results in different ways of timetabling in different universities. Comparing to exact methods which take lots of time to solve UCTP, metaheuristic methods produce a feasible solution within reasonable computati...

This paper presents the first implementation of a generic memetic algorithm on one of the two GPU (Graphic Processing Unit) chips of a GTX295 gaming card. Observed speedups range between ×70 and ×120, mainly depending on the population size. An automatic parallelization of a memetic algorithm is provided through an upgrade of the EASEA language, so that the EC community can benefit from the ext...

Local search techniques have been applied in optimization methods. The effect of local search to the memetic algorithms can make multimodal and non-linear problems easier to solve. Parameters considered include the effect of population size and recombination mechanisms. Experiments comparing three local search techniques for a memetic algorithm are represent. Further, we have adopted a global p...

We propose a new Memetic Particle Swarm Optimization scheme that incorporates local search techniques in the standard Particle Swarm Optimization algorithm, resulting in an efficient and effective optimization method, which is analyzed theoretically. The proposed algorithm is applied to different unconstrained, constrained, minimax and integer programming problems and the obtained results are c...

Dynamic optimization problems challenge traditional evolutionary algorithms seriously since they, once converged, cannot adapt quickly to environmental changes. This paper investigates the application of memetic algorithms, a class of hybrid evolutionary algorithms, for dynamic optimization problems. An adaptive hill climbing method is proposed as the local search technique in the framework of ...

A memetic algorithm is an evolutionary algorithm (EA) augmented with a local search. For many applications, researchers have applied variations of memetic algorithms and have gained very positive experimental results. But the theory of these variations of memetic algorithms is still underdeveloped. This paper defines the (1+1) adaptive memetic algorithm (AMA) with a dynamic mutation probability...