Exam Seating Allocation to Prevent Malpractice Using Genetic Multi-optimization Algorithm

  title={Exam Seating Allocation to Prevent Malpractice Using Genetic Multi-optimization Algorithm},
  author={Madhav Mahesh Kashyap and Saai Ram Thejas and Chandra Gaurav and Kolachina Srinivas},

A Comparison of Genetic Algorithm Operators for the Seat Allocation Problem

This paper compares the performances of various genetic algorithm operators in allocating seats to students for examinations and suggests a crossover operator that can be applied to similar permutation-based problems.

The weighted sum method for multi-objective optimization: new insights

This paper investigates the fundamental significance of the weights in terms of preferences, the Pareto optimal set, and objective-function values and determines the factors that dictate which solution point results from a particular set of weights.

Peripheral visual acuity: Th. Wertheim.

  • T. Wertheim
  • Psychology
    American journal of optometry and physiological optics
  • 1980

Convergence Criteria for Genetic Algorithms

It is shown that by running the genetic algorithm for a sufficiently long time the authors can guarantee convergence to a global optimum with any specified level of confidence, and an upper bound for the number of iterations necessary to ensure this is obtained.

Applying the genetic approach to simulated annealing in solving some NP-hard problems

The authors' approach can be viewed as a simulated annealing algorithm with population-based state transition and with genetic-operator-based quasi-equilibrium control and as a genetic algorithm with the Boltzmann-type selection operator.

Creating seating plans: a practical application

This paper examines the interesting problem of designing seating plans for large events such as weddings and gala dinners where, among other things, the aim is to construct solutions where guests are

New evolutionary genetic algorithms for NP-complete combinatorial optimization problems

The experimental results showed the superiority of new evolutionary algorithms in comparison with the standard genetic algorithm in solving NP-complete combinatorial optimization problems.

An evolutionary approach to the traveling salesman problem

  • D. Fogel
  • Computer Science
    Biological Cybernetics
  • 2004
A simulation of natural evolution is conducted using the traveling salesman problem as an artificial environment and evolutionary adaptation is demonstrated to be worthwhile in a variety of contexts.