This book is concerned with recent advances in fitness landscapes. The concept of fitness landscapes originates from theoretical biology and refers to a framework for analysing and visualizing the relationships between genotypes, phenotypes and fitness. These relationships lay at the centre of attempts to mathematically describe evolutionary processes and evolutionary dynamics. The book addresses recent advances in the understanding of fitness landscapes in evolutionary biology and evolutionary computation. In the volume, experts in the field of fitness landscapes present these findings in an integrated way to make it accessible to a number of audiences: senior undergraduate and graduate students in computer science, theoretical biology, physics, applied mathematics and engineering, but also researcher looking for a reference or/and entry point into using fitness landscapes for analysing algorithms. Also practitioners wanting to employ fitness landscape techniques for evaluating bio- and nature-inspired computing algorithms can find valuable material in the book. For teaching proposes, the book could also be used as a reference handbook.

This book is concerned with recent advances in fitness landscapes. The concept of fitness landscapes originates from theoretical biology and refers to a framework for analysing and visualizing the relationships between genotypes, phenotypes and fitness. These relationships lay at the centre of attempts to mathematically describe evolutionary processes and evolutionary dynamics.

The book addresses recent advances in the understanding of fitness landscapes in evolutionary biology and evolutionary computation. In the volume, experts in the field of fitness landscapes present these findings in an integrated way to make it accessible to a number of audiences: senior undergraduate and graduate students in computer science, theoretical biology, physics, applied mathematics and engineering, but also researcher looking for a reference or/and entry point into using fitness landscapes for analysing algorithms. Also practitioners wanting to employ fitness landscape techniques for evaluating bio- and nature-inspired computing algorithms can find valuable material in the book. For teaching proposes, the book could also be used as a reference handbook.

Ever since the historical discovery of the now-famous Lorenz system in 1963, a large number of nonlinear systems that can produce chaos have been - served,constructedandanalyzed. Infact,chaostheoryhasbecomeindispe- able for science and engineering at all levels of research today. The most - tive recent researchincludes chaos controland chaos synchronization,among others, with a visible trend toward real-world applications. The book titled "Evolutionary Algorithms and Chaotic Systems", edited by Ivan Zelinka, Sergej Celikovsky, Hendrik Richter and Guanrong Chen, is a timely volume to be welcome by the chaos community as well as c- putational intelligence community and beyond. This book is devoted to the studies of common and related subjects in two intensive research ?elds of chaos theory and evolutionary computation. It was not typical that e- lutionary computing techniques are used for e?ective chaos control, chaos synchronization,chaosidenti?cation, andin particularfor chaosanalysisand synthesis, therefore this edition of collective state-of-the-art articles on such interdisciplinarysubjects isespecially valuableforthescienti?candengine- ing communities. For these reasons, I enthusiastically recommend this book to our scientists and engineers working in the ?elds of nonlinear dynamics, evolutionary algorithms, control theory, circuits and systems, and scienti?c computing alike. University of California at Berkeley, September 2009 Leon O. Chua Preface Deterministic chaos is a fairly active area of research in the last few decades. Well known chaotic attractors can even be produced by some simple three-dimensional autonomous systems of ordinary di?erential equations, for example the Lorenz system, which originates from modelling of atmospheric dynamics.

This book constitutes the refereed proceedings of the 22nd European Conference on Genetic Programming, EuroGP 2019, held as part of Evo* 2019, in Leipzig, Germany, in April 2019, co-located with the Evo* events EvoCOP, EvoMUSART, and EvoApplications. The 12 revised full papers and 6 short papers presented in this volume were carefully reviewed and selected from 36 submissions. They cover a wide range of topics and reflect the current state of research in the field. With a special focus on real-world applications in 2019, the papers are devoted to topics such as the test data design in software engineering, fault detection and classification of induction motors, digital circuit design, mosquito abundance prediction, machine learning and cryptographic function design.