Opposition-based Learning (OBL) is a new concept in machine learning, inspired from the
opposite relationship among entities. In 2005, for the first time the concept of opposition was …

Diverse forms of opposition are already existent virtually everywhere around us, and utilizing
opposite numbers to accelerate an optimization method is a new idea. Since 2005 …

The selection of the mutation strategy for differential evolution (DE) algorithm plays an
important role in the optimization performance, such as exploration ability, convergence …

Evolutionary algorithms (EAs) are well-known optimization approaches to deal with
nonlinear and complex problems. However, these population-based algorithms are …

In this paper, an enhanced version of the opposition-based differential evolution (ODE) is
proposed. ODE utilizes opposite numbers in the population initialization and generation …

We propose a novel variation to biogeography-based optimization (BBO), which is an
evolutionary algorithm (EA) developed for global optimization. The new algorithm employs …

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 …

In mammography diagnosis systems, high False Negative Rate (FNR) has always been a
significant problem since a false negative answer may lead to a patient's death. This paper …

Gravitational search algorithm (GSA) is based on the law of gravity and interaction between
masses. In GSA, the searcher agents are a collection of masses, and their interactions are …

This paper presents quasi-oppositional differential evolution to solve reactive power
dispatch problem of a power system. Differential evolution (DE) is a population-based …