Prudent-Daring vs Tolerant Survivor Selection Schemes in Control Design of Electric Drives

Neri, Ferrante; Cascella, Giuseppe Leonardo; Salvatore, N.; Kononova, Anna V.; Acciani, G.

doi:10.1007/11732242_78

Cited by 18 publications

(8 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The parameter ν can vary between 0 and 1 and can be seen as a measurement of the fitness diversity and distribution of the fitness values within the population [35], [21]. More specifically, if ν ≈ 0, the fitness values are similar amongst each other, on the contrary if ν ≈ 1, the fitness values are dissimilar amongst each other and some individuals thus perform much better than the others [36], [22].…”

Section: A Fitness Diversity Adaptation In Mdementioning

confidence: 97%

The “natura non facit saltus” principle in Memetic computing

Tirronen

Neri

Majava

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Self Cite

View full text Add to dashboard Cite

This paper proposes the employment of continuous probability distributions instead of step functions for adaptive coordination of the local search in fitness diversity based Memetic Algorithms. Two probability distributions are considered in this study: the beta and exponential distributions. These probability distributions have been tested within two memetic frameworks present in literature. Numerical results show that employment of the probability distributions can be beneficial and improve performance of the original Memetic Algorithms on a set of test functions without varying the balance between the evolutionary and local search components.

show abstract

Section: A Fitness Diversity Adaptation In Mdementioning

confidence: 97%

The “natura non facit saltus” principle in Memetic computing

Tirronen

Neri

Majava

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Self Cite

View full text Add to dashboard Cite

show abstract

“…ν ∈ [0, 1] can be seen as a measurement of the fitness diversity and distribution of the fitness values within the population [7], [19]. In fact if ν ≈ 0 all the fitness values are similar amongst each other, while if ν ≈ 1 fitness values are different, meaning that some individuals perform much better than others [20], [21].…”

Section: A Memetic Differential Evolutionmentioning

confidence: 97%

Application of Memetic Differential Evolution frameworks to PMSM drive design

Caponio

Neri

Cascella

et al. 2008

2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)

Self Cite

View full text Add to dashboard Cite

This paper proposes the application of Memetic Algorithms employing Differential Evolution as an evolutionary framework in order to achieve optimal design of the control system for a permanent-magnet synchronous motor. Two Memetic Differential Evolution frameworks have been considered in this paper and their performance has been compared to a standard Differential Evolution, a standard Genetic Algorithm and a Memetic Algorithm presented in literature for solving the same problem. All the algorithms have been tested on a simulation of the whole system (control system and plant) using a model obtained through identification tests. Numerical results show that the Memetic Differential Evolution frameworks seem to be very promising in terms of convergence speed and has fairly good performance in terms of final solution detected for the realworld problem under examination. In particular, it should be remarked that the employment of a meta-heuristic local search component during the early stages of the evolution seems to be very beneficial in terms of algorithmic efficiency.

show abstract

“…The index ξ is a fitness based measurement of the phenotypical diversity of the population and it can be seen as a measurement of the state of the phenotypical convergence of the algorithm (see for details [34] and [52]). If ξ ≈ 1 there is a high phenotypical diversity and therefore the convergence conditions are far; if ξ ≈ 0 there is a low phenotypical diversity and means that the convergence is approaching.…”

Section: Adaptive Evolutionary Algorithm With Intelligent Mutation Lomentioning

confidence: 99%

An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV

2007

Self Cite

View full text Add to dashboard Cite

This paper proposes a novel Memetic Algorithm consisting of an Adaptive Evolutionary Algorithm (AEA) with three Intelligent Mutation Local Searchers (IMLSs) for designing optimal multidrug Structured Treatment Interruption (STI) therapies for Human Immunodeficiency Virus (HIV) infection. The AEA is an evolutionary algorithm with a dynamic parameter setting. The adaptive use of the local searchers helps the evolutionary process in the search of a global optimum. The adaptive rule is based on a phenotypical diversity measure of the population. The proposed algorithm has been tested for determining optimal 750-day pharmacological protocols for HIV patients. The pathogenesis of HIV is described by a system of differential equations including a model for an immune response. The multidrug therapies use reverse transcriptase inhibitor and protease inhibitor anti-HIV drugs. The medical protocol designed by the proposed algorithm leads to a strong immune response; the patient reaches a "healthy" state in one and half years and after this the STI medications can be discontinued. A comparison with a specific heuristic method and a standard Genetic Algorithm (GA) has been performed. Unlike the heuristic, the AEA with IMLSs does not impose any restrictions on the therapies in order to reduce the dimension of the problem. Unlike the GA, the AEA with IMLSs can overcome the problem of premature convergence to a suboptimal medical treatment. The results show that the therapies designed by the AEA lead to a "healthy" state faster than with the other methods. The statistical analysis confirms the computational effectiveness of the algorithm.

show abstract

Prudent-Daring vs Tolerant Survivor Selection Schemes in Control Design of Electric Drives

Cited by 18 publications

References 6 publications

The “natura non facit saltus” principle in Memetic computing

The “natura non facit saltus” principle in Memetic computing

Application of Memetic Differential Evolution frameworks to PMSM drive design

An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV

Contact Info

Product

Resources

About

Prudent-Daring vs Tolerant Survivor Selection Schemes in Control Design of Electric Drives

Cited by 18 publications

References 6 publications

The &#x201C;natura non facit saltus&#x201D; principle in Memetic computing

The &#x201C;natura non facit saltus&#x201D; principle in Memetic computing

Application of Memetic Differential Evolution frameworks to PMSM drive design

An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV

Contact Info

Product

Resources

About

The “natura non facit saltus” principle in Memetic computing

The “natura non facit saltus” principle in Memetic computing