This work presents a Genetic Fuzzy Controller (GFC), called Genetic Programming Fuzzy Inference System for Control tasks (GPFIS-Control). It is based on Multi-Gene Genetic Programming, a variant of canonical Genetic Programming. The main characteristics and concepts of this approach are described, as well as its distinctions from other GFCs. Two benchmarks application of GPFIS-Control are considered: the Cart-Centering Problem and the Inverted Pendulum. In both cases results demonstrate the superiority and potentialities of GPFIS-Control in relation to other GFCs found in the literature.

This paper proposes a new approach to Particle Swarm Optimization (PSO) to solve nonlinear problems with linear and nonlinear constraints. A crossover operator and a new particle updating method, named Footholds Concept, were developed to guarantee fully feasible solutions and better search-space coverage, respectively. In addition, a novel swarm initialization heuristic is applied to benchmarks with equality constraints. The algorithm has been tested on 13 common benchmark functions. Experimental results show that it is very competitive as it increases PSO efficiency and improves convergence speed.