JavaEvA 2 A free optimization framework written in Java
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optimize development framework framework algorithm optimization framework evolutionary algorithm

JavaEvA 2 Description

A free optimization framework written in Java JavaEvA 2 is a free, comprehensive heuristic optimization framework with emphasis on evolutionary algorithms implemented in Java. JavaEvA 2 integrates several derivation free optimization methods, preferably population based, such as Evolution Strategies (ES), Genetic Algorithms (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), as well as classical techniques such as multi-start Hill Climbing or Simulated Annealing. Besides typical single-objective problems, multi-modal and multi-objective problem are handled directly by the EvA2 framework. Via the Java mechanism of Remote Method Invocation (RMI), the algorithms of JavaEvA 2 can be distributed over network nodes based on a client-server architecture.JavaEvA 2 is being used as teaching aid in lecture tutorials, as a developing platform in student research projects and applied to numerous optimisation problems within active research and ongoing industrial cooperations. Requirements: · Java What's New in This Release: · The clustering interface has been changed for easier implementation of adaptive clustering methods. · The GradientDescentAlgorithm has been updated and some benchmark problems been made derivable. · The ClusterBasedNiching algorithm has been slightly restructured updated according to the new clustering. · An additional clustering method is included: nearest-best clustering with dynamic adaption of niche radius. · Some changes to (text) statistics: they are now printed regarding the full solution set instead of the last population. The Population field "size" is now termed "targetSize" to avoid misunderstandigs. Populations can be initialized using a Random Latin Hypercube sampling. Some basic console options are recognized: · EvA2 can be started without splash screen and even without GUI. If configuration file is given which was earlier saved from the GUI, the thus defined optimization run is then processed automatically by EvA2.

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