Controlling Bloat in Genetic Programming for Sloving Wall Following Problem

The goal in automatic programming is to get a computer to perform a task by telling it what needs to be done, rather than by explicitly programming it.With considers the task of automatically generating a computer program to enable an autonomous mobile robot to perform the task of following the wall of an irregular shaped room, During the evolution of solutions using genetic programming (GP) there is generally an increase in average tree size without a corresponding increase in fitness—a phenomenon commonly referred to as bloat. Many different bloat control methods have been proposed. This paper review, evaluate, implementation and comparison of these methods in wall following problem and the most appropriate method for solving bloat problem is proposed

Black Holes Algorithm: A Swarm Algorithm inspired of Black Holes for Optimization Problems

In this paper a swarms algorithms, for optimization problem is proposed. This algorithm is inspired of black holes. A black hole is a region of space-time whose gravitational field is so strong that nothing which enters it, not even light, can escape. Every black hole has mass, and charge.  In this Algorithm we suppose each solution of problem as a black hole and use of gravity force for global search and electrical force for local search. The proposed method is verified using several benchmark problems commonly used in the area of optimization. The experimental results on different benchmarks indicate that the performance of the proposed algorithm is better than    PSO (Particle Swarms Optimization), AFS (Artifitial Fish Swarm Algorithm) and RBH-PSO (random black hole particle swarm optimization Algorithm).DOI: http://dx.doi.org/10.11591/ij-ai.v2i3.322

Using Black Holes Algorithm in Discrete Space by Nearest Integer Function

In this paper we Using Black Holes Algorithm in Discrete Space by Nearest Integer Function. Black holes algorithm is a Swarm Algorithm inspired of Black Holes for Optimization Problems. We suppose each solution of problem as an integer black hole and after calculating the gravity and electrical forces use Nearest Integer Function. The experimental results on different benchmarks show that the performance of the proposed algorithm is better than    PSO (Binary Particle Swarms Optimization), and GA (Genetic Algorithm).DOI: http://dx.doi.org/10.11591/ij-ai.v2i4.431