Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
612-822-4611

  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Metaheuristics for Finding Multiple Solutions

Metaheuristics for Finding Multiple Solutions

Preuss, Mike
Epitropakis, Michael G.
Li, Xiaodong

Paperback

Series: Natural Computing

Technology & EngineeringGeneral Computers

ISBN10: 3030795551
ISBN13: 9783030795559
Publisher: Springer Nature
Published: Oct 24 2022
Pages: 315
Weight: 1.01
Height: 0.69 Width: 6.14 Depth: 9.21
Language: English

This book presents the latest trends and developments in multimodal optimization and niching techniques. Most existing optimization methods are designed for locating a single global solution. However, in real-world settings, many problems are multimodal by nature, i.e., multiple satisfactory solutions exist. It may be desirable to locate several such solutions before deciding which one to use. Multimodal optimization has been the subject of intense study in the field of population-based meta-heuristic algorithms, e.g., evolutionary algorithms (EAs), for the past few decades. These multimodal optimization techniques are commonly referred to as niching methods, because of the nature-inspired niching effect that is induced to the solution population targeting at multiple optima. Many niching methods have been developed in the EA community. Some classic examples include crowding, fitness sharing, clearing, derating, restricted tournament selection, speciation, etc.Nevertheless, applying these niching methods to real-world multimodal problems often encounters significant challenges.

Also from

Preuss, Mike

View All

Also in

General Computers

View All