Kenneth O. Stanley is an associate professor in the Department of Electrical Engineering and Computer Science at the University of Central Florida. He received a B.S.E. from the University of Pennsylvania in 1997 and received a Ph.D. in 2004 from the University of Texas at Austin. He is an inventor of the Neuroevolution of Augmenting Topologies (NEAT), HyperNEAT, and novelty search algorithms for evolving complex artificial neural networks. His main research contributions are in neuroevolution (i.e. evolving neural networks), generative and developmental systems (GDS), coevolution, machine learning for video games, and interactive evolution. He has won best paper awards for his work on NEAT, NERO, NEAT Drummer, HyperNEAT, novelty search, and Galactic Arms Race. He is an associate editor of IEEE Transactions on Computational Intelligence and AI in Games, on the editorial board of Evolutionary Computation journal, and on the ACM SIGEVO Executive Committee.  He also co-founded and served as track chair for the GDS track at the ACM SIGEVO-sponsored GECCO conference.

Digital Library Author Page

• Evolving Artificial Neural Networks for Video Games: In recent years interest has grown in the artificial intelligence (AI) community in applying techniques in evolutionary computation and artificial neural networks (ANNs) to video games.  Not only can these technologies potentially enhance the co...
• Evolving Large-Scale Artificial Neural Networks with HyperNEAT: The focus of this talk is on indirectly encoding neural networks through an abstraction of biological development called Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT). The primary conceptual focus will be on the critical role of...
• Evolving Neural Networks: Natural brains are the product of evolution, raising the question of whether it might be possible to evolve brain-like structures through evolutionary algorithms on computers.  This talk presents the field of neuroevolution, which over the last ...
• Generative and Developmental Systems: In evolutionary computation it is common for the genotype to map directly to the phenotype such that each gene in the genotype represents a single discrete component of the phenotype. While this convention is widespread, in nature the mapping is not ...
• New Directions in Interactive Evolutionary Computation: In the branch of evolutionary computation called interactive evolutionary computation (IEC), the user directs the breeding process by selecting the parents of the next generation.  In this talk I survey several recent applications of IEC from my...
• Novelty Search: Searching Without Objectives:

  This talk will contemplate the implications of recent counterintuitive results from experiments with evolutionary algorithms that suggest that search (which is a metaphor for innovation in general) is sometimes most effective when it is not expl...