IJCNN-2013 Tutorial on Evolution of Neural Networks (2013)
Neuroevolution, i.e. evolution of artificial neural networks, has recently emerged as a powerful technique for solving challenging reinforcement learning problems. Compared to traditional (e.g. value-function based) methods, neuroevolution is especially strong in domains where the state of the world is not fully known: The state can be disambiguated through recurrency, and novel situations handled through pattern matching. In this tutorial, I will review (1) neuroevolution methods that evolve fixed-topology networks, network topologies, and network construction processes, (2) ways of combining traditional neural network learning algorithms with evolutionary methods, and (3) applications of neuroevolution to control, robotics, artificial life, and games.

A link to the slides is below.

See also the Scholarpedia article on neuroevolution.

Citation:
To Appear In 2013. Tutorial slides..
Bibtex:

Conference Presentation:
Slides
Risto Miikkulainen Faculty risto [at] cs utexas edu
ENSO This package contains software implementing the ENSO approach for evolving symmetric modular neural networks. It also in... 2010

NEAT C++ The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i... 2010

OpenNERO OpenNERO is a general research and education platform for artificial intelligence. The platform is based on a simulatio... 2010

rtNEAT C++ The rtNEAT package contains source code implementing the real-time NeuroEvolution of Augmenting Topologies method. In ad... 2006

ESP JAVA 1.1 The ESP package contains the source code for the Enforced Sup-Populations system written in Java. This package is a near... 2002

NEAT Java (JNEAT) The JNEAT package contains Java source code for the NeuroEvolution of Augmenting Topologies method (see the original 2002

ESP C++ The ESP package contains the source code for the Enforced Sup-Populations system written in C++. ESP is an extension t... 2000