Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Temporal Heterogeneity Improves Speed and Convergence in Genetic
  Algorithms   Genetic algorithms have been used in recent decades to solve a broad variety
of search problems. These algorithms simulate natural selection to explore a
parameter space in search of solutions for a broad variety of problems. In this
paper, we explore the effects of introducing temporal heterogeneity in genetic
algorithms. In particular, we set the crossover probability to be inversely
proportional to the individual's fitness, i.e., better solutions change slower
than those with a lower fitness. As case studies, we apply heterogeneity to
solve the $N$-Queens and Traveling Salesperson problems. We find that temporal
heterogeneity consistently improves search without having prior knowledge of
the parameter space.

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The categories of this paper are: plasm-ph
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
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The categories of this paper are: patt-sol
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The categories of this paper are: eess
4
The categories of this paper are: cond-mat
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
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The categories of this paper are: nucl-th
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The categories of this paper are: q-alg
2
The categories of this paper are: math
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The categories of this paper are: nlin
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The categories of this paper are: alg-geom
22
The categories of this paper are: chao-dyn