TL;DR: This book discusses the evolution of architecture, primitive functions, terminals, sufficiency, and closure, and the role of representation and the lens effect in genetic programming.
Abstract: Background on genetic algorithms, LISP, and genetic programming hierarchical problem-solving introduction to automatically-defined functions - the two-boxes problem problems that straddle the breakeven point for computational effort Boolean parity functions determining the architecture of the program the lawnmower problem the bumblebee problem the increasing benefits of ADFs as problems are scaled up finding an impulse response function artificial ant on the San Mateo trail obstacle-avoiding robot the minesweeper problem automatic discovery of detectors for letter recognition flushes and four-of-a-kinds in a pinochle deck introduction to biochemistry and molecular biology prediction of transmembrane domains in proteins prediction of omega loops in proteins lookahead version of the transmembrane problem evolutionary selection of the architecture of the program evolution of primitives and sufficiency evolutionary selection of terminals evolution of closure simultaneous evolution of architecture, primitive functions, terminals, sufficiency, and closure the role of representation and the lens effect Appendices: list of special symbols list of special functions list of type fonts default parameters computer implementation annotated bibliography of genetic programming electronic mailing list and public repository
TL;DR: Grammatical evolution is presented, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary string and is compared to genetic programming.
Abstract: We present grammatical evolution, an evolutionary algorithm that can evolve complete programs in an arbitrary language using a variable-length binary string. The binary genome determines which production rules in a Backus-Naur form grammar definition are used in a genotype-to-phenotype mapping process to a program. We demonstrate how expressions and programs of arbitrary complexity may be evolved and compare its performance to genetic programming.
TL;DR: EvoCOMNET Contributions.- Web Application Security through Gene Expression Programming, Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealing, and more.
Abstract: EvoCOMNET Contributions.- Web Application Security through Gene Expression Programming.- Location Discovery in Wireless Sensor Networks Using a Two-Stage Simulated Annealing.- Wireless Communications for Distributed Navigation in Robot Swarms.- An Evolutionary Algorithm for Survivable Virtual Topology Mapping in Optical WDM Networks.- Extremal Optimization as a Viable Means for Mapping in Grids.- Swarm Intelligence Inspired Multicast Routing: An Ant Colony Optimization Approach.- A Framework for Evolutionary Peer-to-Peer Overlay Schemes.- Multiuser Scheduling in HSDPA with Particle Swarm Optimization.- Efficient Signal Processing and Anomaly Detection in Wireless Sensor Networks.- Peer-to-Peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms.- Evolving High-Speed, Easy-to-Understand Network Intrusion Detection Rules with Genetic Programming.- Soft Computing Techniques for Internet Backbone Traffic Anomaly Detection.- Testing Detector Parameterization Using Evolutionary Exploit Generation.- Ant Routing with Distributed Geographical Localization of Knowledge in Ad-Hoc Networks.- Discrete Particle Swarm Optimization for Multiple Destination Routing Problems.- EvoENVIRONMENT Contributions.- Combining Back-Propagation and Genetic Algorithms to Train Neural Networks for Ambient Temperature Modeling in Italy.- Estimating the Concentration of Nitrates in Water Samples Using PSO and VNS Approaches.- Optimal Irrigation Scheduling with Evolutionary Algorithms.- Adaptive Land-Use Management in Dynamic Ecological System.- EvoFIN Contributions.- Evolutionary Money Management.- Prediction of Interday Stock Prices Using Developmental and Linear Genetic Programming.- An Introduction to Natural Computing in Finance.- Evolutionary Approaches for Estimating a Coupled Markov Chain Model for Credit Portfolio Risk Management.- Knowledge Patterns in Evolutionary Decision Support Systems for Financial Time Series Analysis.- Predicting Turning Points in Financial Markets with Fuzzy-Evolutionary and Neuro-Evolutionary Modeling.- Comparison of Multi-agent Co-operative Co-evolutionary and Evolutionary Algorithms for Multi-objective Portfolio Optimization.- Dynamic High Frequency Trading: A Neuro-Evolutionary Approach.- EvoGAMES Contributions.- Decay of Invincible Clusters of Cooperators in the Evolutionary Prisoner's Dilemma Game.- Evolutionary Equilibria Detection in Non-cooperative Games.- Coevolution of Competing Agent Species in a Game-Like Environment.- Simulation Minus One Makes a Game.- Evolving Simple Art-Based Games.- Swarming for Games: Immersion in Complex Systems.- Fitness Diversity Parallel Evolution Algorithms in the Turtle Race Game.- Evolving Strategies for Non-player Characters in Unsteady Environments.- Grid Coevolution for Adaptive Simulations: Application to the Building of Opening Books in the Game of Go.- Evolving Teams of Cooperating Agents for Real-Time Strategy Game.- EvoHOT Contributions.- Design Optimization of Radio Frequency Discrete Tuning Varactors.- An Evolutionary Path Planner for Multiple Robot Arms.- Evolutionary Optimization of Number of Gates in PLA Circuits Implemented in VLSI Circuits.- Particle Swarm Optimisation as a Hardware-Oriented Meta-heuristic for Image Analysis.- EvoIASP Contributions.- A Novel GP Approach to Synthesize Vegetation Indices for Soil Erosion Assessment.- Flies Open a Door to SLAM.- Genetic Image Network for Image Classification.- Multiple Network CGP for the Classification of Mammograms.- Evolving Local Descriptor Operators through Genetic Programming.- Evolutionary Optimization for Plasmon-Assisted Lithography.- An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation.- EvoINTERACTION Contributions.- Interactive Evolutionary Evaluation through Spatial Partitioning of Fitness Zones.- Fractal Evolver: Interactive Evolutionary Design of Fractals with Grid Computing.- Humorized Computational Intelligence towards User-Adapted Systems with a Sense of Humor.- Innovative Chance Discovery - Extracting Customers' Innovative Concept.- EvoMUSART Contributions.- Evolving Approximate Image Filters.- On the Role of Temporary Storage in Interactive Evolution.- Habitat: Engineering in a Simulated Audible Ecosystem.- The Evolution of Evolutionary Software: Intelligent Rhythm Generation in Kinetic Engine.- Filterscape: Energy Recycling in a Creative Ecosystem.- Evolved Ricochet Compositions.- Life's What You Make: Niche Construction and Evolutionary Art.- Global Expectation-Violation as Fitness Function in Evolutionary Composition.- Composing Using Heterogeneous Cellular Automata.- On the Socialization of Evolutionary Art.- An Evolutionary Music Composer Algorithm for Bass Harmonization.- Generation of Pop-Rock Chord Sequences Using Genetic Algorithms and Variable Neighborhood Search.- Elevated Pitch: Automated Grammatical Evolution of Short Compositions.- A GA-Based Control Strategy to Create Music with a Chaotic System.- Teaching Evolutionary Design Systems by Extending "Context Free".- Artificial Nature: Immersive World Making.- Evolving Indirectly Represented Melodies with Corpus-Based Fitness Evaluation.- Hearing Thinking.- EvoNUM Contributions.- Memetic Variation Local Search vs. Life-Time Learning in Electrical Impedance Tomography.- Estimating HMM Parameters Using Particle Swarm Optimisation.- Modeling Pheromone Dispensers Using Genetic Programming.- NK Landscapes Difficulty and Negative Slope Coefficient: How Sampling Influences the Results.- On the Parallel Speed-Up of Estimation of Multivariate Normal Algorithm and Evolution Strategies.- Adaptability of Algorithms for Real-Valued Optimization.- A Stigmergy-Based Algorithm for Continuous Optimization Tested on Real-Life-Like Environment.- Stochastic Local Search Techniques with Unimodal Continuous Distributions: A Survey.- Evolutionary Optimization Guided by Entropy-Based Discretization.- EvoSTOC Contributions.- The Influence of Population and Memory Sizes on the Evolutionary Algorithm's Performance for Dynamic Environments.- Differential Evolution with Noise Analyzer.- An Immune System Based Genetic Algorithm Using Permutation-Based Dualism for Dynamic Traveling Salesman Problems.- Dynamic Time-Linkage Problems Revisited.- The Dynamic Knapsack Problem Revisited: A New Benchmark Problem for Dynamic Combinatorial Optimisation.- Impact of Frequency and Severity on Non-Stationary Optimization Problems.- A Critical Look at Dynamic Multi-dimensional Knapsack Problem Generation.- EvoTRANSLOG Contributions.- Evolutionary Freight Transportation Planning.- An Effective Evolutionary Algorithm for the Cumulative Capacitated Vehicle Routing Problem.- A Corridor Method-Based Algorithm for the Pre-marshalling Problem.- Comparison of Metaheuristic Approaches for Multi-objective Simulation-Based Optimization in Supply Chain Inventory Management.- Heuristic Algorithm for Coordination in Public Transport under Disruptions.- Optimal Co-evolutionary Strategies for the Competitive Maritime Network Design Problem.
TL;DR: A Genetic Algorithm that can evolve complete programs, using a variable length linear genome to govern how a Backus Naur Form grammar definition is mapped to a program, expressions and programs of arbitrary complexity may be evolved.
Abstract: We describe a Genetic Algorithm that can evolve complete programs Using a variable length linear genome to govern how a Backus Naur Form grammar definition is mapped to a program, expressions and programs of arbitrary complexity may be evolved Other automatic programming methods are described, before our system, Grammatical Evolution, is applied to a symbolic regression problem
TL;DR: Grammatical Evolution is the first comprehensive introduction to Grammatical Evolution, a novel approach to Genetic Programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search.
Abstract: From the Publisher:
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language provides the first comprehensive introduction to Grammatical Evolution, a novel approach to Genetic Programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search. Grammatical Evolution's rich modularity gives a unique flexibility, making it possible to use alternative search strategies - whether evolutionary, deterministics or some other approach - and to radically change its behavior by merely changing the grammar supplied. This approach to Genetic Programming represents a powerful new weapon in the Machine Learning toolkit that can be applied to a diverse set of problem domains.