Artificial neural networks (ANNs) have become common data driven tools for modeling complex, nonlinear problems in science and engineering. Many previous applications have relied on gradient-based search techniques, such as the back propagation (BP) algorithm, for ANN training. Such techniques,...

The neutral theory of molecular evolution states that the accumulation of neutral mutations in the genome is fundamental for evolution to occur. The genetic representation of gene expression programming, an artificial genotype/phenotype system, not only allows the existence of non-coding regions...

Previous work on coevolutionary search has demonstrated both successful and unsuccessful applications. As a step in explaining what factors lead to success or failure, we present a comparative study of an evolutionary and a coevolutionary search model. In the latter model, strategies for solving...

Can we build computers that are intelligent and alive? This question has been on the minds of computer scientists since the dawn of the computer age and remains a most compelling line of inquiry. Some would argue that the question makes sense only if we put scare quotes around "intelligent" and...

Fitness distance correlation (FDC) has been offered as a summary statistic with apparent success in predicting the performance of genetic algorithms for global optimization. Here, a counterexample to Hamming-distance based FDC is examined for what it reveals about how GAs work. The...