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RGFGA: An efficient representation and crossover for grouping genetic algorithms
There is substantial research into genetic algorithms that are used to group large numbers of
objects into mutually exclusive subsets based upon some fitness function. However, nearly all
methods involve degeneracy to some degree.
We introduce a new representation for grouping genetic algorithms, the restricted growth function
genetic algorithm, that effectively removes all degeneracy, resulting in a more efficient search. A new crossover operator is also described that exploits a measure of similarity between chromosomes in a population. Using several synthetic datasets, we compare the performance of our representation and crossover with another well known state-of-the-art GA method, a strawman
optimisation method and a well-established statistical clustering algorithm, with encouraging results
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A comparison of general-purpose optimization algorithms forfinding optimal approximate experimental designs
Several common general purpose optimization algorithms are compared for findingA- and D-optimal designs for different types of statistical models of varying complexity,including high dimensional models with five and more factors. The algorithms of interestinclude exact methods, such as the interior point method, the NelderāMead method, theactive set method, the sequential quadratic programming, and metaheuristic algorithms,such as particle swarm optimization, simulated annealing and genetic algorithms.Several simulations are performed, which provide general recommendations on theutility and performance of each method, including hybridized versions of metaheuristicalgorithms for finding optimal experimental designs. A key result is that general-purposeoptimization algorithms, both exact methods and metaheuristic algorithms, perform wellfor finding optimal approximate experimental designs
Population Monte Carlo algorithms
We give a cross-disciplinary survey on ``population'' Monte Carlo algorithms.
In these algorithms, a set of ``walkers'' or ``particles'' is used as a
representation of a high-dimensional vector. The computation is carried out by
a random walk and split/deletion of these objects. The algorithms are developed
in various fields in physics and statistical sciences and called by lots of
different terms -- ``quantum Monte Carlo'', ``transfer-matrix Monte Carlo'',
``Monte Carlo filter (particle filter)'',``sequential Monte Carlo'' and
``PERM'' etc. Here we discuss them in a coherent framework. We also touch on
related algorithms -- genetic algorithms and annealed importance sampling.Comment: Title is changed (Population-based Monte Carlo -> Population Monte
Carlo). A number of small but important corrections and additions. References
are also added. Original Version is read at 2000 Workshop on
Information-Based Induction Sciences (July 17-18, 2000, Syuzenji, Shizuoka,
Japan). No figure
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