Search

A complex model for evolving the update strategy of a Particle Swarm Optimisation (PSO) algorithm is described in this paper. The model is a hybrid technique that combines a Genetic Algorithm (GA) and a PSO algorithm. Each GA chromosome is an array encoding a meaning for updating the particles of the PSO algorithm. The Evolved PSO algorithm is compared to several human-designed PSO algorithms by using ten artificially constructed functions and one real-world problem. Numerical experiments show that the Evolved PSO algorithm performs similarly and sometimes even better than the Standard approaches for the considered problems. The Evolved PSO is highly scalable (regarding the size of the problem's input), being able to solve problems having different dimensions. [ABSTRACT FROM AUTHOR]

Propositional satisfiability (SAT) problem is fundamental to the theory of NP-completeness. Indeed, using the concept of "polynomial-time reducibility" all NP-complete problems can be polynomially reduced to SAT. Thus, any new technique for satisfiability problems will lead to general approaches for thousands of hard combinatorial problems. In this paper, we introduce the incremental propositional satisfiability problem that consists of maintaining the satisfiability of a propositional formula anytime a conjunction of new clauses is added. More precisely, the goal here is to check whether a solution to a SAT problem continues to be a solution anytime a new set of clauses is added and if not, whether the solution can be modified efficiently to satisfy the old formula and the new clauses. We will study the applicability of systematic and approximation methods for solving incremental SAT problems. The systematic method is based on the branch and bound technique while the approximation methods rely on stochastic local search and genetic algorithms. Experimental tests, conducted on randomly generated SAT instances, demonstrate the efficiency in time of the approximation methods over the branch and bound algorithm. However these approximation methods do not always guarantee the completeness of the solution returned. We show that a method we propose that uses non systematic search in a limited form together with branch and bound has the best compromise, in practice, between time and quality of the solution returned (success ratio). [ABSTRACT FROM AUTHOR]

Genetic algorithms have been applied to many optimization and search problems and shown to be very efficient. However, the efficiency of genetic algorithms is not guaranteed in those applications where the search space is small, such as the block motion estimation in video coding applications, or equivalently the chromosome length is relatively short, less than 5 for example. Since the characteristics of these small search space applications are far away from the conventional search problems in which the common genetic algorithms worked well. New treatments of genetic algorithms for dealing with the small range search problems are therefore of interest. In this paper, the efficiency (will be defined later) of the genetic operations of common genetic algorithms, such as crossover and mutation, are analyzed for this special situation. As expected, the so-obtained efficiency/performance of the genetic operations is quite different from that of their traditional counterparts. To fill this gap, a lightweight genetic search algorithm is presented to provide an efficient way for generating near optimal solutions for these kinds of applications. The control overheads of the lightweight genetic search algorithm is very low as compared with that of the conventional genetic algorithms. It is shown by simulations that a lot of computations can be saved by applying the newly proposed algorithm while the search results are still well approved. [ABSTRACT FROM AUTHOR]