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Related Concept Videos

Crossing Over01:30

Crossing Over

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Crossing over is the exchange of genetic information between homologous chromosomes during prophase I of meiosis I. Genetic recombination gives rise to allelic diversity in the newly formed daughter cells. In humans, crossing over produces genetically distinct haploid egg and sperm cells that undergo fertilization to produce unique offspring. Before cell division starts, the germ cell’s chromosome(s) undergo duplication in the S phase of the cell cycle. As the cells enter prophase I,...
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Unlike mitosis, meiosis aims for genetic diversity in its creation of haploid gametes. Dividing germ cells first begin this process in prophase I, where each chromosome—replicated in S phase—is now composed of two sister chromatids (identical copies) joined centrally.
The homologous pairs of sister chromosomes—one from the maternal and one from the paternal genome—then begin to align alongside each other lengthwise, matching corresponding DNA positions in a process...
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Other than maintaining genome stability via DNA repair, homologous recombination plays an important role in diversifying the genome. In fact, the recombination of sequences forms the molecular basis of genomic evolution. Random and non-random permutations of genomic sequences create a library of new amalgamated sequences. These newly formed genomes can determine the fitness and survival of cells. In bacteria, homologous and non-homologous types of recombination lead to the evolution of new...
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In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Genetic Algorithm for Traveling Salesman Problem with Modified Cycle Crossover Operator.

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This study introduces a novel crossover operator for genetic algorithms to solve the Traveling Salesman Problem (TSP). The new operator, combined with path representation, improves solution approximation for this NP-hard optimization problem.

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Area of Science:

  • Computer Science
  • Operations Research
  • Artificial Intelligence

Background:

  • Genetic algorithms (GAs) are evolutionary computation techniques inspired by natural selection.
  • GAs are effective for NP-hard problems, including the Traveling Salesman Problem (TSP).
  • TSP involves finding the shortest possible route that visits a set of cities exactly once and returns to the origin city.

Purpose of the Study:

  • To propose and evaluate a new crossover operator for genetic algorithms applied to the Traveling Salesman Problem.
  • To enhance the efficiency and accuracy of genetic algorithms in finding near-optimal solutions for TSP.
  • To minimize the total distance of the tour in TSP instances.

Main Methods:

  • The study utilizes genetic algorithms, a type of evolutionary algorithm.
  • A novel crossover operator is developed and integrated with the path representation method for TSP.
  • The proposed method is benchmarked against traditional crossover operators like partially mapped and order crossovers.
  • Computational experiments are conducted on standard TSPLIB instances.

Main Results:

  • The new crossover operator, when used with path representation, demonstrates improved performance in approximating TSP solutions.
  • The proposed approach achieved better results compared to existing traditional path representation methods on benchmark instances.
  • The method effectively minimizes the total distance for the Traveling Salesman Problem.

Conclusions:

  • The novel crossover operator offers a promising advancement for solving the Traveling Salesman Problem using genetic algorithms.
  • The integration of the new operator with path representation provides a more natural and effective approach to TSP.
  • This research contributes to the field of combinatorial optimization by improving heuristic methods for NP-hard problems.