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Among the topics are an organizational coevolutionary algorithm for classification, an organizational coevolutionary algorithm for numerical optimization, moving block sequence for a new VSLI floorplan representation, a multiagent genetic algorithm for global numerical optimization, and a multiagent evolutionary algorithm for combinatorial optimization problems.
7]), the following existing hybrid multi-objective evolutionary algorithms are examined: (1) the genetic algorithm running on the internet (GAIN), (2) the parallel single front genetic algorithm (PSFGA), (3) the divided range multi-objective genetic algorithm (DRMOGA), (4) the parallel strength Pareto multi-objective evolutionary algorithm (PSPMEA), (5) the parallel multi-objective evolutionary algorithm with a hyper-graph represented population structure (pMOHypEA), (6) the multi-objective cellular genetic algorithm MOCell, (7) the simple multi-objective genetic local search algorithm (S-MOGLS), (8) the memetic Pareto-archived evolution strategy (M-PAES), (9) the hybrid EA (10) and hybrid NSGA-II (Stanley, Mudge 1995; de Toro et al.
An evolutionary algorithm is a mathematical process where potential solutions keep being improved a step at a time until the optimum is reached,' explains Dr Neumann.
Recently, our work focuses on solving the optimal layout of the sensors using an original evolutionary technique called Guiding Hyperplane Evolutionary Algorithm.
Evolutionary Algorithms for Solving Multi-objective Problems.
Elsewhere, financial systems modelling biologically-inspired evolutionary algorithms are used to analyse complex financial portfolios by companies such as CodeFarm and Icosystem, while BT is investing in alife approaches as a means for understanding and manipulating the behaviour of large, complicated, open socio-technological systems such as wireless networks.
KEEL is a software tool to assess evolutionary algorithms for data mining problems including regression, classification, clustering, pattern mining and so on.
In 2000 Weinert and Mehnen  used an evolutionary algorithm to optimize NURBS surfaces, obtaining good results; though the method was computationally expensive.