With the explosion of data generation, getting optimal solutions to data driven problems is
increasingly becoming a challenge, if not impossible. It is increasingly being recognised that …

This paper surveys strategies applied to avoid premature convergence in Genetic
Algorithms (GAs). Genetic Algorithm belongs to the set of nature inspired algorithms. The …

In evolutionary computation, the fitness function normally measures progress toward an
objective in the search space, effectively acting as an objective function. Through deception …

Genetic algorithms have been used in science and engineering as adaptive algorithms for
solving practical problems and as computational models of natural evolutionary systems …

Neural networks are a computing paradigm that is finding increasing attention among
computer scientists. In this book, theoretical laws and models previously scattered in the …

Genetic algorithms (GAs) are a heuristic search and optimisation technique inspired by
natural evolution. They have been successfully applied to a wide range of real-world …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

This paper establishes a link between the challenge of solving highly ambitious problems in
machine learning and the goal of reproducing the dynamics of open-ended evolution in …

" It is well known that" building blocks", whether they be the atoms of chemistry, the words of
a language, or the modules of a computer, play a key role in our understanding of the world …

A measure of search difficulty, fitness distance correlation (FDC), is introduced and
examined in relation to genetic algorithm (GA) performance. In many cases, this correlation …