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Higher-Order Quantum-Inspired Genetic Algorithms [PDF]

open access: yesarXiv, 2014
This paper presents a theory and an empirical evaluation of Higher-Order Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2) has been presented. Contrary to all QIGA algorithms which represent quantum genes as independent qubits, in higher-order ...
Kucharski, Jacek, Nowotniak, Robert
arxiv   +5 more sources

A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem [PDF]

open access: yesarXiv, 2014
The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, genetic algorithms are well suited for solving the knapsack problem and they find reasonably good solutions quickly.
Dong, Hongbin, He, Feidun, He, Jun
arxiv   +3 more sources

Finite State Machine Synthesis for Evolutionary Hardware [PDF]

open access: yesarXiv, 2013
This article considers application of genetic algorithms for finite machine synthesis. The resulting genetic finite state machines synthesis algorithm allows for creation of machines with less number of states and within shorter time. This makes it possible to use hardware-oriented genetic finite machines synthesis algorithm in autonomous systems on ...
Bereza, Andrey   +2 more
arxiv   +3 more sources

Hybrid quantum search with genetic algorithm optimization [PDF]

open access: yesPeerJ Computer Science
Quantum genetic algorithms (QGA) integrate genetic programming and quantum computing to address search and optimization problems. The standard strategy of the hybrid QGA approach is to add quantum resources to classical genetic algorithms (GA), thus ...
Sebastian Mihai Ardelean, Mihai Udrescu
doaj   +3 more sources

Molecular geometry optimization with a genetic algorithm [PDF]

open access: yes, 1995
We present a method for reliably determining the lowest energy structure of an atomic cluster in an arbitrary model potential. The method is based on a genetic algorithm, which operates on a population of candidate structures to produce new candidates with lower energies.
B. L. Zhang   +16 more
arxiv   +3 more sources

Using a genetic algorithm to find molecules with good docking scores [PDF]

open access: yesPeerJ Physical Chemistry, 2021
A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen molecules from the ZINC database, for four different targets ...
Casper Steinmann, Jan H. Jensen
doaj   +2 more sources

An improved adaptive genetic algorithm [PDF]

open access: yesSHS Web of Conferences, 2022
Genetic algorithm is a classic intelligent bionic algorithm, which is evolved according to the genetic evolution process of organisms in nature, and has strong global optimization ability.
Han Shifen, Xiao Li
doaj   +1 more source

A STUDY ON GENETIC ALGORITHM AND ITS APPLICATIONS

open access: yesInternational Research Journal of Modernization in Engineering Technology and Science, 2023
We need a way to differentiate the best and worse solutions in order to get the best outcomes. A statistical model or simulation could be the metric, or it could be a subjective one where we favor better results over worse ones.
Namira Begum Sayyed
semanticscholar   +1 more source

Perbandingan Fluid Genetic Algorithm dan Genetic Algorithm untuk Penjadwalan Perkuliahan

open access: yesJurnal Sisfokom, 2020
The lecture schedule is a problem that belongs to the NP-Hard problem and multi-objective problem because it has several variables that affect the preparation of the schedule and has limitations that must be met. One solution that has been found is using
Muhammad Ezar Al Rivan   +1 more
doaj   +1 more source

A review on genetic algorithm: past, present, and future

open access: yesMultimedia tools and applications, 2020
In this paper, the analysis of recent advances in genetic algorithms is discussed. The genetic algorithms of great interest in research community are selected for analysis.
Sourabh Katoch, S. Chauhan, Vijay Kumar
semanticscholar   +1 more source

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