Roulette-wheel selection via stochastic acceptance

Lipowski, Adam; Lipowska, Dorota

doi:10.1016/j.physa.2011.12.004

Cited by 616 publications

(261 citation statements)

References 16 publications

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“…5) Select mates: the stochastic universal sampling method [49], which is a fitness proportionate selection method, is used to select candidates that will be used as parents in the crossover process.…”

Section: B Case Study 1: a Control Methods Based On Gamentioning

confidence: 99%

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

Sanfilippo

Hatledal

Pettersen

et al. 2018

IEEE J. Oceanic Eng.

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A benchmark framework for advanced control methods of maritime cranes is presented based on the use of the Functional Mockup Interface (FMI). The system integrates different manipulator models, all the corresponding hydraulic systems, various vessels, and the surrounding environment for visualisation. Different control methods can be transparently implemented and tested. A set of routine tests, different cost functions and metrics are provided -taking into account several factors, including position accuracy, energy consumption, quality, and safety for both the cranes and the surrounding environment. The concept of operation profiles (OP) is introduced, allowing for definition of different standard transporting and lifting operations. By considering task-oriented routines, this benchmark suite allows the comparison of different control methods independently from the specific crane model to be controlled.Two alternative control methods for maritime cranes based on the use of artificial intelligence (AI) are extensively compared. The first method is based on the use of genetic algorithms (GA), while the second method involves the use of particle swarm optimisation (PSO). Simulation results are presented for both methods.

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Section: B Case Study 1: a Control Methods Based On Gamentioning

confidence: 99%

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

Sanfilippo

Hatledal

Pettersen

et al. 2018

IEEE J. Oceanic Eng.

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“…It based on the problem statement and the definition of the concept [6]. Brute force finds the solution of the problem by using a simple, straightforward and obvious way.…”

Section: Brute Force Algorithmmentioning

confidence: 99%

Design and Implementation of Random Word Generator using Backtracking Algorithm for Gameplay in Ambrosia Game

Kuswardayan¹,

Rahman²,

Suciati³

2017

IJCA

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Playing games is one way to spend our free time. Game was created to entertain users who play it. One favorite genre for some gamers is a Role Playing Game (RPG). RPG is favored because players assume the roles of characters in a fictional setting. Game development requires new innovations to fulfill the market's requirement. One of the innovations in the game lies in the gameplay. In the RPG game that adopts Turn Base Strategy (TBS), improvised gameplay is very minimal. TBS system is focus in strategy, while for the action of each player is very little, that makes some novice players feel bored. Therefore, to solve these problems, the TBS gameplay need to improved. Ambrosia is RPG game with TBS gameplay that improved. The improvement gameplay is creating a word from random letters (Anagram) to attack the enemy. The randomization process is considering by the weight of the initial letter. And for extraction of words solution which can be created, is using Backtracking Algorithm. After testing this game, it shows that words solution can extracted by using Backtracking Algorithm. The improved gameplay also successfully attract users to play Ambrosia again.

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“…This paper adopts the roulette wheel selection method [13] to do the selection operation. The selection probability of each individual was calculated according to the fitness function (7) and fitness function (8), and give the equations:…”

Section: Probability Model and Generic Operation 1) Individual Selmentioning

confidence: 99%

Applying Probability Model to The Genetic Algorithm Based Cloud Rendering Task Scheduling

Zhang¹,

Xu²,

Gao³

et al. 2017

International Conferences on Software Engineering and Knowledge Engineering

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Abstract-There are huge amount of tasks and data to be processed in cloud rendering environment. How to effectively schedule them is the key to ensure the overall performance of the cloud rendering environment. In this paper, an improved task scheduling algorithm based on genetic algorithm (PMGA) and probability model is proposed, which aims to minimize the total time and cost of task scheduling . First, the fitness function relating to the total time and task cost is improved under the consideration of the user's satisfaction to the rendering services. Then, a probability model is constructed for the scheduling algorithm, which is used to achieve the non-linear adaptive adjustment of the crossover rate function and the mutation rate function. As a result, the evolutionary ability of poor individuals in the population can be enhanced, avoiding the stagnation in the early stages. Finally, experiments are performed to demonstrate that PMGA has a better ability of optimization than that of traditional adaptive genetic algorithm (AGA). Our approach contributes to reduce the total time and the scheduling cost of the cloud rendering tasks.

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Roulette-wheel selection via stochastic acceptance

Cited by 616 publications

References 16 publications

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

A Benchmarking Framework for Control Methods of Maritime Cranes Based on the Functional Mockup Interface

Design and Implementation of Random Word Generator using Backtracking Algorithm for Gameplay in Ambrosia Game

Applying Probability Model to The Genetic Algorithm Based Cloud Rendering Task Scheduling

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