Open AccessBook
Foundations in Grammatical Evolution for Dynamic Environments
Reads0
Chats0
TLDR
'Foundations in Grammatical Evolution for Dynamic Environments' is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments.Abstract:
Dynamic environments abound, encompassing many real-world problems in fields as diverse as finance, engineering, biology and business. A vibrant research literature has emerged which takes inspiration from evolutionary processes to develop problem-solvers for these environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is a cutting edge volume illustrating current state of the art in applying grammar-based evolutionary computation to solve real-world problems in dynamic environments. The book provides a clear introduction to dynamic environments and the types of change that can occur. This is followed by a detailed description of evolutionary computation, concentrating on the powerful Grammatical Evolution methodology. It continues by addressing fundamental issues facing all Evolutionary Algorithms in dynamic problems, such as how to adapt and generate constants, how to enhance evolvability and maintain diversity. Finally, the developed methods are illustrated with application to the real-world dynamic problem of trading on financial time-series. The book was written to be accessible to a wide audience and should be of interest to practitioners, academics and students, who are seeking to apply grammar-based evolutionary algorithms to solve problems in dynamic environments. 'Foundations in Grammatical Evolution for Dynamic Environments' is the second book dedicated to the topic of Grammatical Evolution.read more
Citations
More filters
Journal ArticleDOI
Grammar-based Genetic Programming: a survey
TL;DR: This work surveys the various grammar-based formalisms that have been used in GP and discusses the contributions they have made to the progress of GP, showing how grammar formalisms contributed to the solutions of these problems.
Journal ArticleDOI
Open issues in genetic programming
TL;DR: Some of the challenges and open issues that face researchers and practitioners of GP are outlined and it is hoped this overview will stimulate debate, focus the direction of future research to deepen the understanding of GP, and further the development of more powerful problem solving algorithms.
Journal ArticleDOI
Grammatical Evolution of Local Search Heuristics
TL;DR: This paper presents a grammatical evolution methodology which automatically designs good quality local search heuristics that maintain their performance on new problem instances.
Journal ArticleDOI
Population-based De Novo Molecule Generation, Using Grammatical Evolution
Naruki Yoshikawa,Kei Terayama,Kei Terayama,Masato Sumita,Teruki Homma,Kenta Oono,Koji Tsuda,Koji Tsuda +7 more
TL;DR: In this article, a population-based approach using a gram-based algorithm was proposed to generate new and promising drug candidates. But it is not suitable for the use of synthetic data.
Journal ArticleDOI
Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods
J. Ignacio Hidalgo,J. Manuel Colmenar,Gabriel Kronberger,Stephan M. Winkler,Oscar Garnica,Juan Lanchares +5 more
TL;DR: An enhanced genetic programming algorithm is proposed that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.