Algorithms for crossover point determination

Efficient Approximation Algorithms for Point-set Diameter in Higher Dimensions

We study the problem of computing the diameter of a  set of $n$ points in $d$-dimensional Euclidean space for a fixed dimension $d$, and propose a new $(1+varepsilon)$-approximation algorithm with $O(n+ 1/varepsilon^{d-1})$ time and $O(n)$ space, where $0 < varepsilonleqslant 1$. We also show that the proposed algorithm can be modified to a $(1+O(varepsilon))$-approximation algorithm with $O(n+...

One-Point Geometric Crossover

Uniform crossover for binary strings has a natural geometric interpretation that allows us to generalize it rigorously to any search space endowed with a notion of distance and any representation [6]. In this paper, we present an analogous characterization for one-point crossover and explicitly derive formally specific one-point crossovers for a number of well-known representations.

A Novel Crossover Operator for Genetic Algorithms: Ring Crossover

The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A GA allows a population composed of many individuals to evolve under specified selection rules to a state that maximizes the “fitness” function. In that process, crossover operator plays an important role. To comprehend the GAs as a whole, it is necessary to understand ...

Determination of Crossover Counts

Central Point Crossover for Neuro-genetic Hybrids

In this paper, we consider each neural network as a point in a multi-dimensional problem space and suggest a crossover that locates the central point of a number of neural networks. By this, genetic algorithms can spend more time around attractive areas. We also apply representational normalization to neural networks to maintain genotype consistency in crossover. For the normalization, we utili...