...short introduction...

Crossover, mutation, and evolution of a population are the basic principles of the evolution and natural selection. Evolutionary algorithms (EA) imitate the nature’s behaviour, and adapt the basic principles, to evolve a set of solutions, towards the optimum solution of a problem.

mPOEMS is an optimisation framework, in the domain of evolutionary algorithms. It was designed to solve optimisation problems, with an unrestricted number of objectives. The algorithm has shown excellent performance in comparison with other state-of-the-art search algorithms. The paper including these results can be found using google scholar.

The drawback of Evolutionary Algorithms is their complexity, and the development effort one has to conduct to create an implementation of such an algorithm. An impressing approach to test and compare various evolutionary algorithms, with little programming effort, is presented by the PISA project. The project website can be found by following this link.

PISA is a platform and programming language independent interface for search algorithms. The use of PISA ensure easy, fast and reliable comparison of different optimization algorithms on various problems or benchmarks. With litte programming effort, one can create the problem specific part of an optimisation problem, and combine it with arbitrary ready-to-use, pre-compiled and very complex optimisation search algorithms.

The project "mPOEMS in JAVA" shares the goal of providing an easy-to-use framework, to use complex search algorithms, with PISA. The PISA framework is applicable to almost any search algorithm. mPOEMS in JAVA focus on providing a framework to use the evolutionary algorithm mPOEMS, without the need to know the algorithm in detail.

Details, a documentation of the framework, templates and sources are presented in the detail page.

Some links are presented below.

...papers about (m)POEMS...

• Jiri Kubalik, Richard Mordinyi and Stefan Biffl: Multiobjective Prototype Optimization with Evolved Improvement Steps
  Springer Link
• Jiri Kubalik and Jan Faigl: Iterative Prototype Optimisation with Evolved Improvement Steps
  Springer Link
• Jiri Kubalik: Real-Parameter Optimization by Iterative Prototype Optimization with Evolved Improvement Steps
  IEEEXplore

...PISA references...

• Stefan Bleuler, Marco Laumanns, Lothar Thiele, Eckart Zitzler: PISA - A Platform and Programming Language Independent Interface for Search Algorithms
  Citeseerx
• PISA Homepage
  Link

...search on google scholar...

• for the paper presenting the evolutionary algorithm mPOEMS
  search term "mpoems kubalik"
• for the paper presenting PISA
  search term "pisa interface search algorithms"

...further information...

• Wikipedia Article about Evolutionary Algorithms
• Introduction to Genetic Algorithms