ECJ A Java-based Evolutionary Computation Research System
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ECJ Tags

Number Computation mathematical computation computation model computation Computation Override polynomial computation symbolic computation R6RS computation evolutionary process Computation details scientific computation geometric computation algebra computation Computation Services numerical computation Evolutionary Algorithm create evolutionary algorithm develop evolutionary algorithm evolutionary tree radiation computation accurate computation specify evolutionary algorithm Parallel Computation data-intensive computation incremental cut computation evolutionary computation environment distribute computation Computation Research System Evolutionary Research System Research System Evolutionary Computation evolutionary clade VAR computation p-value computation evolutionary simulation simulate evolutionary genetics evolutionary trace method stated density computation evolutionary pattern analysis evolutionary biology tool evolutionary data viewer analyze evolutionary data mY estimators computation Computation Framework

ECJ Description

ECJ is a research EC system written in Java. It was designed to be highly flexible, with nearly all classes (and all of their settings) dynamically determined at runtime by a user-provided parameter file. All structures in the system are arranged to be easily modifiable. Even so, the system was designed with an eye toward efficiency. Get ECJ and give it a go to see how useful it can actually be for you! Main features: General Features: GUI with charting Platform-independent checkpointing and logging Hierarchical parameter files Multithreading Mersenne Twister Random Number Generators Abstractions for implementing a variety of EC forms. EC Features: Asynchronous island models over TCP/IP Master/Slave evaluation over multiple processors, with support for generational, asynchronous steady-state, and coevolutionary distribution Genetic Algorithms/Programming style Steady State and Generational evolution, with or without Elitism Evolutionary-Strategies style (mu,lambda) and (mu+lambda) evolution Very flexible breeding architecture Many selection operators Multiple subpopulations and species Inter-subpopulation exchanges Reading populations from files Single- and Multi-population coevolution SPEA2 multiobjective optimization Particle Swarm Optimization Differential Evolution Spatially embedded evolutionary algorithms Hooks for other multiobjective optimization methods Packages for parsimony pressure GP Tree Representations: Set-based Strongly-Typed Genetic Programming Ephemeral Random Constants Automatically-Defined Functions and Automatically Defined Macros Multiple tree forests Six tree-creation algorithms Extensive set of GP breeding operators Eight pre-done GP application problem domains (ant, regression, multiplexer, lawnmower, parity, two-box, edge, serengeti) Vector (GA/ES) Representations: Fixed-Length and Variable-Length Genomes Arbitrary representations Ten pre-done vector application problem domains (rastrigin, sum, rosenbrock, sphere, step, noisy-quartic, booth, griewangk, nk, hiff) Other Representations: Multiset-based genomes in the rule package, for evolving Pitt-approach rulesets or other set-based representations.

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