The question of whether studies of human learning provide evidence for distinct conscious and unconscious influences remains as controversial today as ever. Much of this controversy arises from the use of the logic of dissociation. The controversy has prompted the use of an alternative approach that places conscious and unconscious influences on memory retrieval in opposition. Here we ask wheth...

Over the years, opposition-based Learning (OBL) technique has been proven to effectively enhance convergence of meta-heuristic algorithms. The fact that OBL is able give alternative candidate solutions in one or more opposite directions ensures good exploration and exploitation search space. In last decade, many techniques have established literature including Standard-OBL, General-OBL, Quasi R...

Diverse forms of opposition are already existent virtually everywhere around us but the nature and significance of oppositeness is well understood only in specific contexts within the fields of philosophy, linguistics, psychology, logic and physics. The interplay between entities and opposite entities is apparently fundamental for maintaining universal balance. However, it seems that there is a...

For many soft computing methods, we need to generate random numbers to use either as initial estimates or during the learning and search process. Recently, results for evolutionary algorithms, reinforcement learning and neural networks have been reported which indicate that the simultaneous consideration of randomness and opposition is more advantageous than pure randomness. This new scheme, ca...

Estimation of distribution algorithms are a class of optimization algorithms based on probability distribution model. In this paper, we propose an improved estimation of distribution algorithm using opposition-based learning and Gaussian copulas. The improved algorithm employs multivariate Gaussian copulas to construct probability distribution model and uses opposition-based learning for popula...