Evolutionary Computation
This page contains resources about Genetic Algorithms, Evolutionary Computation, Swarm Intelligence and Population-Based Metaheuristics in general.
More specific information is included in each subfield.
Subfields and Concepts
- Evolutionary Algorithms
- Genetic Algorithms
- NeuroEvolution of Augmenting Topologies (NEAT) for the generation of evolving Artificial Neural Networks
- Genetic Programming
- Particle Swarm Optimization
- Swarm Algorithms
- Ant Colony Optimization
- Evolutionary Inductive Turing Machines
- Evolutionary Automata
Online Courses
Video Lectures
Lecture Notes
Books and Book Chapters
See also Bibliography
- Sheppard, C. (2016). Genetic Algorithms with Python. CreateSpace.
- Luke, S. (2009). Essentials of Metaheuristics. Lulu.
- Poli, R., Langdon, W. B., McPhee, N. F., & Koza, J. R. (2008). A field guide to genetic programming. Lulu.
- Sivanandam, S. N., & Deepa, S. N. (2008). Introduction to genetic algorithms. Springer.
- Brabazon, A., & O'Neill, M. (2006). Biologically inspired algorithms for financial modelling. Springer Science & Business Media.
- Haupt, R. L., & Haupt, S. E. (2004). Practical genetic algorithms. John Wiley & Sons.
- Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing. Springer Science & Business Media.
- Bäck, T., Fogel, D. B., & Michalewicz, Z. (Eds.). (2000). Evolutionary computation 1: Basic algorithms and operators (Vol. 1). CRC Press.
- Bäck, T., Fogel, D. B., & Michalewicz, Z. (Eds.). (2000). Evolutionary computation 12: Advanced Algorithms and Operators (Vol. 2). Taylor & Francis.
- Mitchell, T. M. (1997). "Chapter 9: Genetics Algorithms". Machine Learning. McGraw Hill.
- Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
Scholarly Articles
Software
See also
Other Resources
- Evolutionary Computation - Google Scholar Metrics (Top Publications)
- Evolutionary Computation - Notebook
- GeneticAlgorithmsWithPython (GitHub) - code of the book GAWP by Sheppard
- NEAT: An Awesome Approach to NeuroEvolution - blog post