Extending Population-Based Incremental Learning to Continuous Search Spaces

TL;DR: In this review, the argument starts out with large population sizes, reflecting recent extensions of the CMA algorithm, and similarities and differences to continuous Estimation of Distribution Algorithms are analyzed.

TL;DR: This paper summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space.

TL;DR: The authors summarizes the research on population-based probabilistic search algorithms based on modeling promising solutions by estimating their probability distribution and using the constructed model to guide the exploration of the search space.

TL;DR: Estimation of distribution algorithms are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions and many of the different types of EDAs are outlined.

TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.

TL;DR: In this article, the authors present a comprehensive introduction to the theory and practice of artificial intelligence for modern applications, including game playing, planning and acting, and reinforcement learning with neural networks.

TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.

TL;DR: Introduction PART I: A COMPARISON of EVOLUTIONARY ALGORITHMS 1. Organic Evolution and Problem Solving 2. Specific Evolutionary Algorithms 3. Artificial Landscapes 4. An Empirical Comparison 5. Selection 6. Mutation 7. An Experiment in Meta-Evolution