Representations for genetic and evolutionary algorithms

By Franz Rothlauf. Studies in Fuzziness and Soft Computing, 2002

670 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/29bf944cbbb8c4affe151069644d624fe87c413c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

Citations in the corpus

Global Optimization Algorithms -- Theory and Application (2009, methodology): “Bäck [274]: Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms Bäck, Fogel, and Michalewicz [275]: Handbook of Evolutionary Computation Fogel [442]: Evolutionary Computation: The Fossil Record Deb [12]: Multi-Objective Optimization Usin”
Intelligent web-based learning system with personalized learning path guidance (2008, methodology): “The most common used method of reproduction operation is the weighted roulette selection scheme (Rothlauf, 2002) and the scheme was also employed to perform reproduction operation in this study.”
Evolutionary computation and structural design: A survey of the state-of-the-art (2005, positive): “Initial framework for evolutionary representation theory has been recently proposed by Rothlauf [96], but it is just the beginning of research on this important topic in EC.”
Stochastic Transportation Network Design Problem with Spatial Equity Constraint (2004, background): “In GAs, decision variables can be coded as binary, integer, or real representations (14).”
Semantically-based crossover in genetic programming: application to real-valued symbolic regression (2011, background): “Previous evolutionary computation research has shown that characteristics of evolutionary operators such as their constructiveness, locality (small changes in genotype resulting in small changes in phenotype), and effect on population diversity strongly affect the performance of the resulting algori”
Experience-Driven Procedural Content Generation (2011, background): “The study of representations in evolutionary computation is a broad field in its own right, where several concepts have originated that bear on PCG [100].”
Search-Based Procedural Content Generation: A Taxonomy and Survey (2011, methodology): “Although PCG has been successfully applied to a range of creative domains, we shall focus on its application to games in this survey.”
Automatic programming: The open issue? (2019, background): “More generally, as identified in the field of evolutionary computation, important features in the design of representation include locality, redundancy and scaling of alleles [56].”
Bio-inspired computation: Where we stand and what's next (2019, background): “Regarding the former, an interesting line stems from the derivation of heterogeneous search algorithms merging different encoding strategies, bio-inspired operators and constraint handling techniques [235] into a single unified search algorithm suited for parallel implementations [236].”
Abandoning Objectives: Evolution Through the Search for Novelty Alone (2011, background): “…from the traditional deﬁnition (Goldberg, 1987) and is not meant to trivialize the impact or difﬁculty of choosing the correct representation (Rothlauf and Goldberg, 2002), parameters (De Jong, 2006), or search operators (Yao, 1993), all of which affect the performance of the EA and the…”