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 …

Data are being generated and used to support all aspects of healthcare provision, from
policy formation to the delivery of primary care services. Particularly, with the change of …

Data-driven analysis methods, such as the information content of a fitness sequence,
characterize a discrete fitness landscape by quantifying its smoothness, ruggedness, or …

In this paper, a new genetic programming (GP) algorithm for symbolic regression problems
is proposed. The algorithm, named statistical genetic programming (SGP), uses statistical …

The inherent difficulty of solving a continuous, static, bound-constrained and single-objective
black-box optimization problem depends on the characteristics of the problem's fitness …

The problem of algorithm selection, that is identifying the most efficient algorithm for a given
computational task, is non-trivial. Meta-learning techniques have been used successfully for …

Predicting the performance of Genetic Programming (GP) helps us identify whether it is an
appropriate approach to solve the problem at hand. However, previous studies show that …

Changes in environment are common in daily activities and can introduce new problems. To
be adaptive to these changes, new solutions are to be found every time change occur. This …

What constitutes a hard optimization problem to an Evolutionary Algorithm (EA)? To answer
the question, the study of Fitness Landscape (FL) has emerged as one of the most …

Genetic programming (GP) is an evolutionary algorithm that has received a lot of attention
lately due to its success in solving hard world problems. There has been a lot of interest in …