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About 10,000 papers for “hybrid algorithm”

Hybrid Algorithm

Aris Gkoulalas-Divanis, Vassilios S. Verykios · ˜The œKluwer international series on advances in database systems · 2010

Cited by 0

The HybrID algorithm.

Lucas Helms (3858001), Jeff Clune (53764) · Figshare · 2017

<p>Individuals are initially indirectly encoded (here, with HyperNEAT) to exploit problem regularities. At the encoding switch point (dashed vertical line), individuals are converted to a direct encoding (here, FT-NEAT) to further improve performance by handling irregularities of the problem. Adapted from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0174635#pone.0174635.ref024" target="_blank">24</a>].</p>

Cited by 0Open access

Hybrid Encryption Algorithm

Gaurav R. Patel, Prof Krunal Panchal · 2015

Abstract- Cryptography is derived from a Greek word which means, the art of protecting information by transforming it into an unreadable format. In order to prevent some unwanted users or people to get access to the data cryptography is needed. This paper introduces hybrid approaches by combining two most important algorithms RSA algorithm and Diffie Hellman algorithm. This hybrid encryption algorithm provides more security as compare to RSA algorithm. The implementation and result is also derived in the paper.

Cited by 0

Hybrid Genetic Algorithm

Kedar Nath Das · Advances in computational intelligence and robotics book series · 2013

Real coded Genetic Algorithms (GAs) are the most effective and popular techniques for solving continuous optimization problems. In the recent past, researchers used the Laplace Crossover (LX) and Power Mutation (PM) in the GA cycle (namely LX-PM) efficiently for solving both constrained and unconstrained optimization problems. In this chapter, a local search technique, namely Quadratic Approximation (QA) is discussed. QA is hybridized with LX-PM in order to improve its efficiency and efficacy. The generated hybrid system is named H-LX-PM. The supremacy of H-LX-PM over LX-PM is validated through a test bed of 22 unconstrained and 15 constrained typical benchmark problems. In the later part of this chapter, a few applications of GA in networking optimization are highlighted as the scope for future research.

Cited by 7

Hybrid Genetic Algorithm (HGA)

Lahoz-Beltra Rafael · Figshare · 2016

Hybrid Genetic Algorithm (HGA) is a GA that combines quantum operators (rotation, measure, quantum chromosomes, etc.) with classical genetic operators (crossover and mutation).It can be used for the purposes of education and research. HGA is applied in a simple optimization problem:<br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0&lt;=x&lt;=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).

Cited by 1Open access

Hybrid Genetic Algorithm (HGA)

Lahoz-Beltra, Rafael · Figshare · 2016

Hybrid Genetic Algorithm (HGA) is a GA that combines quantum operators (rotation, measure, quantum chromosomes, etc.) with classical genetic operators (crossover and mutation).It can be used for the purposes of education and research. HGA is applied in a simple optimization problem:<br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0&lt;=x&lt;=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).

Cited by 0Open access

Hybrid CPU Scheduling Algorithm

Anil Kumar Gupta · 2016

Abstract — The purpose of multiprogramming is to maximize the CPU utilization through some process running at all times in the CPU. A process change its state during the time of its execution that may be in one of the following states queue (new, ready, waiting, running, terminated) in the operating system. The procedure of selecting processes among these state queues is carried out by a scheduler. Scheduling of CPU is one of the critical factors that affect the efficiency and the efficiency of the system is maximized when we allocate processes to processor in a precise manner in process scheduling. The goal of CPU scheduler is to allocate processes to be executed by the processor. In this paper we proposed a new simple algorithm that is both pre-emptive and non-preemptive in nature to find a solution for CPU Scheduling. This algorithm is based on the NOVEL Algorithm [1]. Our aim is to enhance the novel algorithm and minimize average waiting time &amp;amp; average turnaround time for the given number of processes and result of this algorithm is then compared with

Cited by 0

Hybrid ABFA-APSO Algorithm

Sabo Miya Hassan, Rosdiazli Ibrahim, Nordin Saad et al. · Studies in systems, decision and control · 2020

Cited by 7

Hybrid Page Replacement Algorithm

Egbunu Charity O, Ogedengbe Matthew T, Ollo Vincent · Zenodo (CERN European Organization for Nuclear Research) · 2019

Page replacement algorithms play essential roles in virtual memory management, especially in Operating Systems that uses paging. Pagereplacement occurs when a requested page is not found in the memory (page fault) or a free available page is not enough to satisfy the request. This is because there are none, or the number of free pages is lower than the number requested. In this study, two standard page replacement (Least Recently Used LRU and OptimalOPT algorithms) were hybridized as hybrid page replacement (HRA). HRA based on the concept of OPT and LRU for it operation. The HRA was evaluatedby comparingit performance in terms of number of page faults generated with the standard page replacement algorithms, namely First InFirst Out FIFO, LRU and Optimal. Results showed that HRA outperformedFIFO, OPT and LRU when the number of frames increases to 4 and above..

Cited by 0Open access

Hybrid Pelican Komodo Algorithm

Purba Daru Kusuma, Ashri Dinimaharawati · Telkom University · 2022

In this work, a new metaheuristic algorithm, namely the hybrid pelican Komodo algorithm (HPKA), has been proposed. This algorithm is developed by hybridizing two shortcoming metaheuristic algorithms: the Pelican Optimization Algorithm (POA) and Komodo Mlipir Algorithm (KMA).

Cited by 0Open access

A hybrid firefly algorithm

Qi XB(亓祥波), Zhu, Sihan, Zhang H(张浩) · SIA OpenIR (Chinese Academy of Sciences) · 2017

Firefly Algorithm (FA) is a bio-inspired algorithm simulating the flashing behavior of fireflies. In original algorithm, all the fireflies are unisex and only the flashing behavior is simulated. This paper presents a novel hybrid firefly algorithm (HFA) adding mating behavior in original firefly algorithm. The new algorithm is compared with FA and other three well-known bio-inspired algorithms on fifteen benchmark functions. The experimental results show HFA has a good optimizing ability on most benchmark functions and is proved to have significant improvement over canonical FA and several other comparison algorithms.

Cited by 0

HYBRID GENETIC ALGORITHM (HGA)

Lahoz-Beltra, Rafael · Figshare · 2016

Hybrid Genetic Algorithm (HGA) is a GA that combines quantum operators (rotation, measure, quantum chromosomes, etc.) with classical genetic operators (crossover and mutation).It can be used for the purposes of education and research. HGA is applied in a simple optimization problem:<br><br>Let f(x)=abs(x-5/2+sin(x)) be a function that takes values in the range 0&lt;=x&lt;=15. Within this range f(x) has a maximum value at x=11 (binary value is equal to 1011).

Cited by 1Open access