Tackling 0/1 knapsack problem with gene induction

Bhatia, A. K.; Basu, Swapan

doi:10.1007/s00500-002-0240-4

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A heuristic mutation operator has been proposed in Bhatia and Basu (2003) for the 0/1 knapsack problem. It identifies a dominant gene with greedy heuristic and induces it in a chromosome with a given probability of mutation.…”

Section: Gene Inductionmentioning

confidence: 99%

See 1 more Smart Citation

A naive genetic approach for non-stationary constrained problems

Basu

Bhatia

2005

Soft Comput

Self Cite

View full text Add to dashboard Cite

An algorithm to be effective for solving nonstationary problems should be robust, adaptive to the changing environment and efficient. Genetic algorithms (GAs) are increasingly being used to solve non-stationary problems. We use GA with a new approach of gene induction (Bhatia and Basu in Soft Comput 8(1):1-9, 2003) to solve non-stationary constrained problems. The approach combines high value genes to form chromosomes from the initial population itself. The efficacy of the method is demonstrated on non-stationary versions of 0/1 knapsack and pure-integer programming problems. The results obtained with the approach are compared with those obtained with feedback thermo

show abstract

Section: Gene Inductionmentioning

confidence: 99%

“…It has been demonstrated in Bhatia and Basu (2003) that the standard GA fails to solve all the instances of a constrained problem. Therefore, it is not suitable for solving non-stationary problems.…”

Section: Introductionmentioning

confidence: 99%

A naive genetic approach for non-stationary constrained problems

Basu

Bhatia

2005

Soft Comput

Self Cite

View full text Add to dashboard Cite

show abstract

“…These authors discussed how to determine effective parameters for GAs developed for 0/1 KPs using the Taguchi method, and traced how the optimum values of the parameters vary according to the structure of the problem. Bhatia and Basu [2] proposed a gene induction approach for GAs.…”

Section: Introductionmentioning

confidence: 99%

Solving the 0/1 knapsack problem using rough sets and genetic algorithms

Yang¹,

Wang²

2011

Journal of the Chinese Institute of Industrial Engineers

View full text Add to dashboard Cite

This article proposes a methodology that introduces attribute reduction of rough sets into crossover of genetic algorithms (GAs), and then uses the methodology to develop two algorithms. The first algorithm selects the crossover points, either by attribute reduction or randomly; the second selects the crossover points solely by attribute reduction, with no crossover otherwise. We test the methodology on the solving of the 0/1 knapsack problem, due to the problem's NP-hard complexity, and we compare the experiment results to those of typical GAs. According to the results, the introduction of attribute reduction increases the mean and decreases the standard deviation of the final solutions, especially in the presence of tighter capacity, i.e. attribution reduction leads to better solution quality and more tightly clustered solutions. Moreover, the mean number of iterations required to terminate the algorithm and that required to reach maximal profits are significantly reduced.

show abstract

Mitigating Deception in Genetic Search Through Suitable Coding

Basu

Bhatia

2006

Neural Information Processing

View full text Add to dashboard Cite

Tackling 0/1 knapsack problem with gene induction

Cited by 6 publications

References 10 publications

A naive genetic approach for non-stationary constrained problems

A naive genetic approach for non-stationary constrained problems

Solving the 0/1 knapsack problem using rough sets and genetic algorithms

Mitigating Deception in Genetic Search Through Suitable Coding

Contact Info

Product

Resources

About