Distance based parameter adaptation for Success-History based Differential Evolution

Viktorin, Adam; Šenkeřík, Roman; Pluháček, Michal; Kadavý, Tomáš; Zamuda, Aleš

doi:10.1016/j.swevo.2018.10.013

Cited by 107 publications

(35 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Among the existing CI algorithms for large number of parameters, Differential Evolution (DE) [116] variants like the recent DISH [132] algorithm could be exploited. DE algorithm was introduced in 1995 by Storn and Price [116] and since then formed a basis for a set of successful algorithms for optimization domains, such as continuous, discrete, mixed-integer, or other search spaces and features [146].…”

Section: Parameter Estimationmentioning

confidence: 99%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

show abstract

Section: Parameter Estimationmentioning

confidence: 99%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among the existing implementations of metaheuristic algorithms for fuzzy rules optimization, DE [66] modifications such as DISH [75] and others [18,50,51] can be considered. DE has been thoroughly investigated with an emphasis on the theoretical insight and insights into inner population dynamics [81,76,68,46].…”

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

“…For this reason, we expect these advanced versions of DE to be effective for the fuzzy rules optimization problem, especially in high dimensional applications. One of the newest DE algorithms is the Success-History based Adaptive Differential Evolution (SHADE) [67], which has a line of recent improvements following JADE [83] that is based on jDE [9], upgraded as L-SHADE [69], SPS-L-SHADE-EIG [28], LSHADE-cnEpSin [3], jSO [11], aL-SHADE [49], and most recently, DISH [75]. To make the paper selfcontained we describe the canonical DE followed by necessary improvements leading to the most recent DISH version.…”

Section: Metaheuristics For Fuzzy Rulesmentioning

confidence: 99%

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Korytkowski

Šenkeřík

Scherer

et al. 2019

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

View full text Add to dashboard Cite

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

show abstract

“…As a thriving application platform, HPC excels in supporting execution and it's speedup through parallellisation when running Computational Intelligence (CI) algorithms. The likes of CI algorithms supported by this action includes development of some of most efficient optimization algorithms for continuous optimization as defined with benchmark functions competition framework from Congress on Evolutionary Computation (CEC) 2017 [143,144]. Specifically useful, in [144] a Differential Evolution (DE) algorithm is enhanced with a new mechanism, the distance based parameter adaptation in the context of Success-History based DE (SHADE), the winner strategy of several previous CEC competitions.…”

Section: Hpc-enabled Modelling and Simulation For Socio-economical Anmentioning

confidence: 99%

“…The likes of CI algorithms supported by this action includes development of some of most efficient optimization algorithms for continuous optimization as defined with benchmark functions competition framework from Congress on Evolutionary Computation (CEC) 2017 [143,144]. Specifically useful, in [144] a Differential Evolution (DE) algorithm is enhanced with a new mechanism, the distance based parameter adaptation in the context of Success-History based DE (SHADE), the winner strategy of several previous CEC competitions. An important contribution of an expert system for underwater glider path planning using DE was published in [145], where the application of SHADE strategy enabled significant advances in improved path planning over mesoscale ocean current structures.…”

Section: Hpc-enabled Modelling and Simulation For Socio-economical Anmentioning

confidence: 99%

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Grelck

Niewiadomska-Szynkiewicz

Aldinucci

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Modelling and Simulation (M&S) offer adequate abstractions to manage the complexity of analysing big data in scientific and engineering domains. Unfortunately, big data problems are often not easily amenable to efficient and effective use of High Performance Computing (HPC) facilities and technologies. Furthermore, M&S communities typically lack the detailed expertise required to exploit the full potential of HPC solutions while HPC specialists may not be fully aware of specific modelling and simulation requirements and applications. The COST Action IC1406 High-Performance Modelling and Simulation for Big Data Applications has created a strategic framework to foster interaction between M&S experts from various application domains on the one hand and HPC experts on the other hand to develop effective solutions for big data applications. One of the tangible outcomes of the COST Action is a collection of case studies from various computing domains. Each case study brought together both HPC and M&S experts, giving witness of the effective cross-pollination facilitated by the COST Action. In this introductory article we argue why joining forces between M&S and HPC communities is both timely in the big data era and crucial for success in many application domains. Moreover, we provide an overview on the state of the art in the various research areas concerned.

show abstract

Distance based parameter adaptation for Success-History based Differential Evolution

Cited by 107 publications

References 32 publications

Towards Human Cell Simulation

Towards Human Cell Simulation

Efficient Image Retrieval by Fuzzy Rules from Boosting and Metaheuristic

Why High-Performance Modelling and Simulation for Big Data Applications Matters

Contact Info

Product

Resources

About