Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 64. Chapters: Almeida-Pineda recurrent backpropagation, Bootstrap aggregating, CN2 algorithm, Constrained conditional model, Constructing skill trees, Dehaene-Changeux model, Diffusion map, Diffusion maps, Dominance-based rough set approach, Dynamic time warping, Error-driven learning, Evolutionary multi-modal optimization, Expectation-maximization algorithm, FastICA, Forward-backward algorithm, GeneRec, Genetic Algorithm for Rule Set Production, Gene expression programming, Growing self-organizing map, HEXQ, IDistance, K-nearest neighbor algorithm, Kernel principal component analysis, Leabra, Linde-Buzo-Gray algorithm, List of machine learning algorithms, Local outlier factor, LogitBoost, Manifold alignment, Margin Infused Relaxed Algorithm, Minimum redundancy feature selection, Non-negative matrix factorization, Online machine learning, Prefrontal Cortex Basal Ganglia Working Memory, PU learning, PVLV, Q-learning, Quadratic unconstrained binary optimization, Randomized weighted majority algorithm, Reinforcement learning, Rprop, SARSA, Skill chaining, Sparse PCA, Stochastic gradient descent, Temporal difference learning, Wake-sleep algorithm. Excerpt: Gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models. These computer programs are complex tree structures that learn and adapt by changing their sizes, shapes, and composition, much like a living organism. And like living organisms, the computer programs of GEP are also encoded in simple linear chromosomes of fixed length. Thus, GEP is a genotype-phenotype system, benefiting from a simple genome to keep and transmit the genetic information and a complex phenotype to explore the environment and adapt to it. GEP has been criticized for not being a major improvement over other genetic programming techniques. In many...
