Year-end Sale % OFF 
Abstract
Fitness landscapes were proposed in 1932 as an abstract notion for understanding biological evolution and were later used to explain evolutionary algorithm behaviour. The last ten years has seen the field of fitness landscape analysis develop from a largely theoretical idea in evolutionary computation to a practical tool applied in optimisation in general and more recently in machine learning. With this widened scope, new types of landscapes have emerged such as multiobjective landscapes, violation landscapes, dynamic and coupled landscapes and error landscapes. This survey is a follow-up from a 2013 survey on fitness landscapes and includes an additional 11 landscape analysis techniques. The paper also includes a survey on the applications of landscape analysis for understanding complex problems and explaining algorithm behaviour, as well as algorithm performance prediction and automated algorithm configuration and selection. The extensive use of landscape analysis in a broad range of areas highlights the wide applicability of the techniques and the paper discusses some opportunities for further research in this growing field.
Highlights
IntroductionThis survey is a follow-up from a previous survey published in Information Sciences journal in 2013 [1]
The article states “despite extensive research on fitness landscape analysis and a large number of developed techniques, very few techniques are used in practice . . . It is hoped that this survey will invoke renewed interest in the field of understanding complex optimisation problems and lead to better decision making on the use of appropriate metaheuristics.” [1]
It would be interesting to investigate whether landscape analysis could be used in the context of generative adversarial networks (GANs) to better understand the dynamics of adversarial training
Summary
This survey is a follow-up from a previous survey published in Information Sciences journal in 2013 [1]. The hope of renewed interest in the field of fitness landscape analysis has been realised, evident in the increase in the number of published papers on the topic as well as the appearance of tutorials, workshops and special sessions dedicated to this topic at all the major evolutionary computation conferences. One of the changes that has emerged in the last few years is that the notion of fitness landscapes has been extended to include new types of landscapes such as multiobjective fitness landscapes, violation landscapes, dynamic and coupled landscapes and error or loss landscapes in the context of neural network training
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
