591 results on '"Riccardo Poli"'
Search Results
152. Crossover Operators For A Hardware Implementation Of GP Using FPGAs And Handel-C.
153. On The Search Biases Of Homologuous Crossover In Linear Genetic Programming And Variable-length Genetic Algorithms.
154. Using Schema Theory To Explore Interactions Of Multiple Operators.
155. A Fixed Point Analysis Of A Gene Pool GA With Mutation.
156. Models of Performance of Evolutionary Program Induction Algorithms Based on Indicators of Problem Difficulty.
157. General Schema Theory for Genetic Programming with Subtree-Swapping Crossover.
158. A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representations.
159. Exact Schema Theorems for GP with One-Point and Standard Crossover Operating on Linear Structures and Their Application to the Study of the Evolution of Size.
160. A schema theory analysis of mutation size biases in genetic programming with linear representations.
161. Exact GP schema theory for headless chicken crossover and subtree mutation.
162. Online real-time platform for microrobot steering in a multi-bifurcation
163. The Effects of Constant and Bit-Wise Neutrality on Problem Hardness, Fitness Distance Correlation and Phenotypic Mutation Rates.
164. A genetic programming approach to the evolution of brain-computer interfaces for 2-D mouse-pointer control.
165. Hyperschema Theory for GP with One-Point Crossover, Building Blocks, and Some New Results in GA Theory.
166. Recursive Conditional Schema Theorem, Convergence and Population Sizing in Genetic Algorithms.
167. Exact Schema Theorem and Effective Fitness for GP with One-Point Crossover.
168. Neutrality in evolutionary algorithms... What do we know?
169. Geometric crossover for the permutation representation.
170. Evolution of human-competitive lossless compression algorithms with GP-zip2.
171. Practical performance models of algorithms in evolutionary program induction and other domains.
172. Theoretical results in genetic programming: the next ten years?
173. Editorial to tenth anniversary issue on progress in genetic programming and evolvable machines.
174. Sub-machine-code GP: New Results and Extensions.
175. Boolean Functions Fitness Spaces.
176. Genetic Programming Discovers Efficient Learning Rules for the Hidden and Output Layers of Freeforward Neural Networks.
177. Smooth Uniform Crossover with Smooth Point Mutation in Genetic Programming: A Preliminary Study.
178. Parallel genetic algorithm taxonomy.
179. Dynamic Demes parallel genetic algorithm.
180. Evolutionary computation teaching at Birmingham.
181. Semiotic Aspects of Generalized Bases of Data.
182. Learning a Navigation Task in Changing Environments by Multi-task Reinforcement Learning.
183. Analogue evolutionary brain computer interfaces [Application Notes].
184. Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework.
185. Mean and Variance of the Sampling Distribution of Particle Swarm Optimizers During Stagnation.
186. Dual Network Representation Applied to the Evolution of Neural Controllers.
187. Efficient Evolution of Asymmetric Recurrent Neural Networks Using a PDGP-inspired Two-Dimensional Representation.
188. Genetic Programming Bloat with Dynamic Fitness.
189. Fitness Causes Bloat: Mutation.
190. A Review of Theoretical and Experimental Results on Schemata in Genetic Programming.
191. Mapping non-conventional extensions of genetic programming.
192. Parameter Mapping: A genetic programming approach to function optimization.
193. An Eigen analysis of the GP community.
194. An Experimental Analysis of Schema Creation, Propagation and Disruption in Genetic Programming.
195. Evolution of Graph-Like Programs with Parallel Distributed Genetic Programming.
196. Parallel Distributed Genetic Programming Applied to the Evolution of Natural Language Recognisers.
197. Discovery of Symbolic, Neuro-Symbolic and Neural Networks with Parallel Distributed Genetic Programming.
198. Continual Robot Learning with Constructive Neural Networks.
199. Particle swarm optimization.
200. Evolving Problems to Learn About Particle Swarm Optimizers and Other Search Algorithms.
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