Bibliography
Evolutionary Computation-related publications are widely diffused within and beyond the most prestigious journals dedicated to the topic (see also the ‘Journals’ section of this site). EC-related bibliography can be searched for in the main bibliographic repositories, such as ISI Web of Science, Scopus, IEEExplore, etc.
A relevant independent effort to keep track of publications dedicated to Genetic Programming is the Genetic Programming Bibliography, a virtually exhaustive collection of the publications on GP from John Koza’s seminal book (and even earlier) onwards.
Springer Book Series
Lecture Notes in Computer Science
This conference proceedings series publishes the latest research developments in all areas of computer science – quickly, informally and at a high level. Evostar’s Proceedings are published in this series. Together with its subseries LNAI & LNBI, LNCS volumes are indexed in the ISI Conference Proceedings Citation Index, Scopus, EI Engineering Index, Google Scholar, DBLP, etc.
Natural Computing Series.
Most books on EC-related topics are published in the Natural Computing Series. This series includes monographs, textbooks, and state-of-the-art collections covering the whole spectrum of Natural Computing and ranging from theory to applications.
Genetic and Evolutionary Computation Series
The Genetic and Evolutionary Computation Book Series publishes research monographs, edited collections, and graduate-level texts on this rapidly growing field. Areas of coverage include applications, theoretical foundations, technique extensions and implementation issues of all areas of genetic and evolutionary computation including genetic algorithms, genetic programming, evolution strategies, evolutionary programming, learning classifier systems and other variants of genetic and evolutionary computation.
EC free software
ECJ — Evolutionary Computation in Java
ECJ is a research EC system written in Java. It was designed to be highly flexible, with nearly all classes (and all of their settings) dynamically determined at runtime by a user-provided parameter file. All structures in the system are arranged to be easily modifiable. Even so, the system was designed with an eye toward efficiency.
DEAP — Distributed Evolutionary Algorithms in Python
DEAP is an evolutionary computation framework for rapid prototyping and testing of ideas. It seeks to make algorithms explicit and data structures transparent, well compatible with parallelization mechanisms such as multiprocessing and SCOOP.
HeuristicLab
HeuristicLab is a .NET-based framework for heuristic and evolutionary algorithms that is developed by members of the Heuristic and Evolutionary Algorithms Laboratory (HEAL) since 2002.
Java Computing Library for Evolutionary Computation
JCLEC is a software system for Evolutionary Computation (EC) research, developed in the Java programming language. It provides a high-level software framework to do any kind of Evolutionary Algorithm (EA), providing support for genetic algorithms (binary, integer and real encoding), genetic programming (Koza’s style, strongly typed, and grammar based) and evolutionary programming.
Evolving Objects (EO): an Evolutionary Computation Framework
EO is a template-based, ANSI-C++ evolutionary computation library which allows one to write stochastic optimization algorithms very fast. It is possibly a bit outdated, but still popular.
GAlib: a C++ Library of Genetic Algorithm Components
GAlib contains a set of C++ genetic algorithm objects. The library includes tools for using genetic algorithms to do optimization in any C++ program using any representation and genetic operators. Like in the previous case, although it is slightly outdated, it is still a very popular package.
Evostar
EvoStar is the reason for the existence of SPECIES. An almost 20-year long running conference and a reference for the European (and not only) community of EC researchers.
Evostar’s proceedings are published by Springer in the LNCS series.