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EvA2 Documentation

The software package EvA2 is a modular framework with an inherent client server structure to solve practical optimization problems. It was especially designed to develop and test new approaches for Evolutionary Algorithms and to use them in real-world applications.

EvA2 provides implementations of most common Evolutionary Algorithms and related (meta-)heuristics such as: Genetic Algorithms, Evolution Strategies, Differential Evolution, Particle Swarm Optimization, Tribes, CHC Adaptive Search, Population Based Incremental Learning, Model-Assisted Evolution Strategies, Genetic Programming and Grammatical Evolution. Still the modular framework of EvA2 allows everyone to add their own optimization modules to meet their specific requirements.

EvA2 uses a generic GUI framework that allows GUI access to any member of a class if get/set methods are provided and an editor is defined for the given data type. This approach allows very fast development cycles, since nearly no additional effort is necessary for implementing GUI elements, while user specific GUI elements can still be developed and integrated to increase usability.

The EvA2 Short Documentation gives an overview over the EvA2-GUI and helps you optimizing your own new problem with EvA2. There are three main ways to achieve this:

• using an external interface of EvA2, such as system calls or Matlab,
• implement it extending based on a given Java example class, or
• implement it extending a given Java abstract problem class.

Using an external interface is usually the simplest way, as you need to know hardly anything about EvA internals. Using the Java example class is still very easy for anyone knowing Java, while using the abstract problem class increases the possibilities to interact with the GUI, for example, but also requires understanding some more concepts on how EvA2 works. So, depending on your goals, you may choose the approach that fits the most and let the documentation guide you through it.

The slightly outdated Technical Report on JavaEvA may be interesting if you want to go into details or implement your own optimization technique. Yet for a start we recommend reading the short documentation first.

Title	Type
EvA2 Short Documentation (pdf) [BibTeX]	Manual (PDF, 2008-2011)
EvA2 Short Documentation (html)	Manual (HTML, 2008-2011)
JavaEvA: A Java based framework for Evolutionary Algorithms [BibTeX]	Technical Report (PDF, 2005)