A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization

Jabeen, Hajira; Baig, Abdul Rauf

doi:10.1007/978-3-642-13769-3_7

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…It is the simplest form of logistic equations. The mathematical expression of the map is shown in (1), where x i ∈ (0,1) is a chaotic variable which constitutes the ratio of the current population to the highest population.…”

Section: Chaotic Systemmentioning

confidence: 99%

“…GP has been used successfully by many researchers for solving the classification problems of improving the accuracy of the classifiers. Jabeen et al [1] has proposed a method that increases the accuracy of the classifiers with less number of function evaluations using a single GP classifier. It also optimizes the evolved intelligent structures using Particle Swarm Optimization technique.…”

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confidence: 99%

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Evolutionary Method o f Detecting Intrusions u sing Different Population Dynamics

Guruprasad*,

G. L.

2020

IJITEE

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Chaos theory plays a vital role in any evolutionary based algorithms for avoiding the local optima and to improve the convergence speed. Various researchers have used different methods to increase the detection rate and to speed up the convergence. Some researchers have used evolutionary algorithms for the same purpose and has proved that the application of those algorithms provide good results. Most of the researchers have used population sizes which remains constant throughout the evolution. It has been seen that small population size may result in premature convergence and large population size requires more computation time to find a solution. In this paper, a novel application of different population dynamics to the genetic programming (GP) algorithm has been applied to manage the population size. The main focus was to improve the accuracy of the normal GP algorithm by varying the population sizes at each generation. The experiments were conducted on the standard GP algorithm using static and dynamic population sizes. Different population dynamics has been used to check the effectiveness of the proposed algorithm. The results obtained has shown that dynamic population size gives better results compared to static population size and also solves the problem of local optima.

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Section: Chaotic Systemmentioning

confidence: 99%

mentioning

confidence: 99%

Evolutionary Method o f Detecting Intrusions u sing Different Population Dynamics

Guruprasad*,

G. L.

2020

IJITEE

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“…From the updating formula (5), we can see that the particles obey the inertia of their own firstly to retain part of their own attributes ⋅ and then update their behavior according to the best cognitive ability of the environment of their own ; the social cognitive ability of particle movement is the global best position [8,9]. This mechanism optimization reflects people's actual behavior reasonably.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Passengers’ Evacuation in Ships Based on Neighborhood Particle Swarm Optimization

Yuan

Zhang

Liu

et al. 2014

Mathematical Problems in Engineering

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A new intelligent model to simulate evacuation behavior in ships called neighborhood particle swarm optimization is proposed. This model determines the rules of behavior and velocity updating formulas to solve staff conflicts. The individuals in evacuation are taken as particles in PSO and update their behaviors by individual attributes, neighborhood attributes, and social attributes. Putting the degree of freedom movement of ships into environment factor and using the real Ro-Ro ship information and IMO test scenarios to simulate the evacuation process, the model in this paper can truly simulate the behavior of persons in emergency and provide a new idea to design excellent evacuation model.

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“…Finally, many hybridizations of SI with other automatic programming techniques can be found in the literature, for many goals: self‐adapt the mutation rate in linear GP, improve the power of crossover operator in GP, optimize GP‐evolved arithmetic classifier expressions, optimize parameters and connected weights of ANNs, etc. However, to our knowledge, only one proposal exists the other way round, presented by Hara et al, where genetic operators were used to change the structure of individuals in AP.…”

Section: Open Issuesmentioning

confidence: 99%

“…Finally, many hybridizations of SI with other automatic programming techniques can be found in the literature, for many goals: self-adapt the mutation rate in linear GP, 129 improve the power of crossover operator in GP, 130 optimize GP-evolved arithmetic classifier expressions, 131 optimize parameters and AP, ant programming; ACP, ant colony programming; PSP, particle swarm programming; BSP, bee swarm programming; AFSP, artificial fish swarm programming; FP, firefly programming; HP, herd programming; GAP, generalized ant programming; EGAP, enhanced generalized ant programming; CAP, cartesian AP; GS, grammatical swarm; TSO, tree swarm optimization; ABCP, artificial bee colony programming; GBC, grammatical bee colony; GFA, geometric firefly algorithm; GE, Grammatical evolution; GBAP, grammar-based ant programming; MOGBAP, multi-objective grammar-based ant programming; APIC, AP for imbalanced classification; TAG, tree-adjoining grammar; ACO, ant colony optimization; PSO, particle swarm optimization; GPSO, geometric PSO; G3P, grammar-guided GP; GEP, gene expression programming; GP, genetic programming; DAP, dynamic ant programming; GDE, grammatical differential evolution; ARM, association rule mining; RARM, rare association rule mining;.…”

Section: Open Issuesmentioning

confidence: 99%

Swarm‐based metaheuristics in automatic programming: a survey

Olmo

Romero

Ventura

2014

WIREs Data Min & Knowl

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On the one hand, swarm intelligence (SI) is an emerging field of artificial intelligence that takes inspiration in the collective and social behavior of different groups of simple agents. On the other hand, the automatic evolution of programs is an active research area that has attracted a lot of interest and has been mostly promoted by the genetic programming paradigm. The main objective is to find computer programs from a high‐level problem statement of what needs to be done, without needing to know the structure of the solution beforehand. This paper looks at the intersection between SI and automatic programming, providing a survey on the state‐of‐the‐art of the automatic programming algorithms that use an SI metaheuristic as the search technique. The expression of swarm programming (SP) has been coined to cover swarm‐based automatic programming proposals, since they have been published to date in a disorganized manner. Open issues for future research are listed. Although it is a very recent area, we hope that this work will stimulate the interest of the research community in the development of new SP metaheuristics, algorithms, and applications. WIREs Data Mining Knowl Discov 2014, 4:445–469. doi: 10.1002/widm.1138 This article is categorized under: Algorithmic Development > Association Rules Technologies > Classification Technologies > Computational Intelligence

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A Framework for Optimization of Genetic Programming Evolved Classifier Expressions Using Particle Swarm Optimization

Cited by 6 publications

References 19 publications

Evolutionary Method o f Detecting Intrusions u sing Different Population Dynamics

Evolutionary Method o f Detecting Intrusions u sing Different Population Dynamics

Passengers’ Evacuation in Ships Based on Neighborhood Particle Swarm Optimization

Swarm‐based metaheuristics in automatic programming: a survey

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