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Remembering the future: An overview of co-evolution in musical improvisation
Musical improvisation is driven mainly by the unconscious mind, engaging the dialogic imagination to reference the entire cultural heritage of an improvisor in a single flash. This paper introduces a case study of evolutionary computation techniques, in particular genetic co-evolution, as applied to the frequency domain using MPEG7 techniques, in order to create an artificial agent that mediates between an improvisor and her unconscious mind, to probe and unblock improvisatory action in live music performance or practice
Theory and practice of population diversity in evolutionary computation
Divergence of character is a cornerstone of natural evolution. On the contrary, evolutionary optimization processes are plagued by an endemic lack of population diversity: all candidate solutions eventually crowd the very same areas in the search space. The problem is usually labeled with the oxymoron âpremature convergenceâ and has very different consequences on the different applications, almost all deleterious. At the same time, case studies from theoretical runtime analyses irrefutably demonstrate the benefits of diversity. This tutorial will give an introduction into the area of âdiversity promotionâ: we will define the term âdiversityâ in the context of Evolutionary Computation, showing how practitioners tried, with mixed results, to promote it. Then, we will analyze the benefits brought by population diversity in specific contexts, namely global exploration and enhancing the power of crossover. To this end, we will survey recent results from rigorous runtime analysis on selected problems. The presented analyses rigorously quantify the performance of evolutionary algorithms in the light of population diversity, laying the foundation for a rigorous understanding of how search dynamics are affected by the presence or absence of diversity and the introduction of diversity mechanisms
Advances in Evolutionary Algorithms
With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field
Automatic General of a Neural Network Architecture Using Evolutionary Computation
This paper reports the application of evolutionary computation in the automatic generation of a neural network architecture. It is a usual practice to use trial and error to find a suitable neural network architecture. This is not only time consuming but may not generate an optimal solution for a given problem. The use of evolutionary computation is a step towards automation in architecture generation. In this paper a brief introduction to the field is given as well as an implementation of automatic neural network generation using genetic programmin
The 1/5-th Rule with Rollbacks: On Self-Adjustment of the Population Size in the GA
Self-adjustment of parameters can significantly improve the performance of
evolutionary algorithms. A notable example is the
genetic algorithm, where the adaptation of the population size helps to achieve
the linear runtime on the OneMax problem. However, on problems which interfere
with the assumptions behind the self-adjustment procedure, its usage can lead
to performance degradation compared to static parameter choices. In particular,
the one fifth rule, which guides the adaptation in the example above, is able
to raise the population size too fast on problems which are too far away from
the perfect fitness-distance correlation.
We propose a modification of the one fifth rule in order to have less
negative impact on the performance in scenarios when the original rule reduces
the performance. Our modification, while still having a good performance on
OneMax, both theoretically and in practice, also shows better results on linear
functions with random weights and on random satisfiable MAX-SAT instances.Comment: 17 pages, 2 figures, 1 table. An extended two-page abstract of this
work will appear in proceedings of the Genetic and Evolutionary Computation
Conference, GECCO'1
EvoPER-An R package for applying evolutionary computation methods in the parameter estimation of individual-based models implemented in Repast
Individual-based models are complex and they normally have an elevated number of input parameters
which must be tuned in order to reproduce the experimental or observed data as accurately as possible.
Hence one of the weakest points of such kind of models is the fact that rarely the modeler has the enough
information about the correct values or even the acceptable range for the input parameters. Therefore,
several parameter combinations must be checked to find an acceptable set of input factors minimizing the
deviations of simulated and observed data. In practice, most of the times, is computationally unfeasible to
traverse the complete search space to check all parameter combination in order to find the best of them.
That is precisely the kind of combinatorial problem suitable for evolutionary computation techniques.
In this work we present the EvoPER, an R package for simplifying the parameter estimation using
evolutionary computation techniques. The current version of EvoPER includes implementations of PSO,
SA and ACO algorithms for parameter estimation of models generated with the open source agent-based
modeling toolkit Repast
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