Evolutionary algorithms are bio-inspired algorithms based on Darwin’s theory of evolution. They are expected to provide non-optimal but good quality solutions to problems whose resolution is impracticable by exact methods.
In six chapters, this book presents the essential knowledge required to efficiently implement evolutionary algorithms.
Chapter 1 describes a generic evolutionary algorithm as well as the basic operators that compose it. Chapter 2 is devoted to the solving of continuous optimization problems, without constraint. Three leading approaches are described and compared on a set of test functions. Chapter 3 considers continuous optimization problems with constraints. Various approaches suitable for evolutionary methods are presented. Chapter 4 is related to combinatorial optimization. It provides a catalog of variation operators to deal with order-based problems. Chapter 5 introduces the basic notions required to understand the issue of multi-objective optimization and a variety of approaches for its application. Finally, Chapter 6 describes different approaches of genetic programming able to evolve computer programs in the context of machine learning.
Betal enkelt med kort, Klarna, Apple Pay eller Google Pay. Ikke fornøyd? Du har alltid 14 dagers angrerett. Les mer i våre vilkår. Har du spørsmål, send oss en e-post på hello@memmo.org.
Memmo gjør det enklere å studere – uansett hvor du er i verden. Hos oss samler du pensumbøker og smarte studieverktøy på ett og samme sted: sammendrag, quizer, podkaster og flashcards. Og så Ted, din studiekompis som svarer på alt du lurer på. Over 50 000 studenter studerer allerede her – bygget for at du skal lære raskere og stresse mindre.