Selecting the most appropriate algorithm to use when attempting to solve a black-box
continuous optimization problem is a challenging task. Such problems typically lack …

Genetic programming (GP) is not a field noted for the rigor of its benchmarking. Some of its
benchmark problems are popular purely through historical contingency, and they can be …

Over past few decades, as a powerful analytical tool to characterize the fitness landscape of
a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all …

Heuristic methods are used when rigorous ones are either unknown or cannot be applied,
typically because they would be too slow. A metaheuristic is a general optimization …

The performance of differential evolution (DE) algorithm significantly depends on mutation
strategy. However, there are six commonly used mutation strategies in DE. It is difficult to …

We present the results of a community survey regarding genetic programming benchmark
practices. Analysis shows broad consensus that improvement is needed in problem …

Several methods to incorporate semantic awareness in genetic programming have been
proposed in the last few years. These methods cover fundamental parts of the evolutionary …

It is approximately 50 years since the first computational experiments were conducted in
what has become known today as the field of Genetic Programming (GP), twenty years since …

Multimodal optimization problems (MMOPs) require the algorithms to find multiple global or
local optima in a single run, which is considered a difficult task. In recent years, niching …

Over the last two decades, many different differential evolution algorithms for solving
optimization problems have been introduced. Although most of these algorithms used a …