Benefits of a Population: Five Mechanisms That Advantage Population-Based Algorithms

Prügel-Bennett, Adam

doi:10.1109/tevc.2009.2039139

Cited by 72 publications

(64 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The chosen algorithmic structure is that of an Evolutionary Algorithm (EA), see [24] because the long and discontinuous binary structure requires a high diversity which can be achieved by using a population based system, see [25]. The algorithmic features are listed in the following.…”

Section: A Metaheuristic Approach: a Tailored Evolutionary Algorithmmentioning

confidence: 99%

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Cochez

Neri

2015

2015 IEEE Symposium Series on Computational Intelligence

View full text Add to dashboard Cite

Abstract-Exact methods for Agglomerative Hierarchical Clustering (AHC) with average linkage do not scale well when the number of items to be clustered is large. The best known algorithms are characterized by quadratic complexity. This is a generally accepted fact and cannot be improved without using specifics of certain metric spaces. Twister tries is an algorithm that produces a dendrogram (i.e, outcome of a hierarchical clustering) which resembles the one produced by AHC, while only needing linear space and time. However, twister tries are sensitive to rare, but still possible, hash evaluations. These might have a disastrous effect on the final outcome. We propose the use of a metaheuristic algorithm to overcome this sensitivity and show how approximate computations of dendrogram quality can help to evaluate the heuristic within reasonable time. The proposed metaheuristic is based on an evolutionary framework and integrates a surrogate model of the fitness within it to enhance the algorithmic performance in terms of computational time.

show abstract

Section: A Metaheuristic Approach: a Tailored Evolutionary Algorithmmentioning

confidence: 99%

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Cochez

Neri

2015

2015 IEEE Symposium Series on Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Note that the FA is population-based. Population-based algorithms have the following advantages when compared to single-point search algorithms [12]: a Building blocks are put together from different solutions through crossover. b…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

Gao

Zhang

2016

Energies

View full text Add to dashboard Cite

Abstract:With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.

show abstract

“…For the specific parameters of M = 100 and k = 7 on the Concatenated-V problem, [105] state that a hill-climber will achieve an expected final fitness of 366 ± 5 with a probability of finding the optimal all-ones solution (fitness of 400) as 5 × 10 −19 .…”

Section: Number Of Generationsmentioning

confidence: 99%

“…. , x N } is a bit-vector composed of x i ∈ {0, 1}.Unfortunately the extremely simple nature of this problem does not demonstrate the utility of GA well [105] and since incremental search works well, GA tends to revert into 6.2 Methodology 175 a hill climber by gaining benefit predominantly from mutation only. This biases the population towards local search behaviour, meaning the population is largely similar and so mostly similar performing.…”

mentioning

confidence: 99%

“…The main justification for this choice, rather than Genetic Programming (GP) or within the IDGP implementation, is it simplifies analysis by avoiding confounding unrelated issues generated from other aspects such as environmental effects of being in situ and the complexities of being distributed and GP on motes (see Section 3.6). Furthermore, prior research on this problem by others [105] employed GA, so an understanding of how GA performs on the problem already exists in the literature. …”

mentioning

confidence: 99%

See 1 more Smart Citation

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

Valencia¹

View full text Add to dashboard Cite

This research presents In situ Distributed Genetic Programming (IDGP) as a framework for distributively evolving logic while attempting to maintain acceptable average performance on highly resource-constrained embedded networked devices.The framework is motivated by the proliferation of devices employing microcontrollers with communications capability and the absence of online learning approaches that can evolve programs for them. Swarm robotics, Internet of Things (IoT) devices including smart phones, and arguably the most constrained of the embedded systems, Wireless Sensor Networks (WSN) motes, all possess the capabilities necessary for the distributed evolution of logic -specifically the abilities of sensing, computing, actuation and communications. Genetic programming (GP) is a mechanism that can evolve logic for these devices using their "native" logic representation (i.e. programs) and so technically GP could evolve any behaviour that can be coded on the device.IDGP is designed, implemented, demonstrated and analysed as a framework for evolving logic via genetic programming on highly resource-constrained networked devices in real-world environments while achieving acceptable average performance.Designed with highly resource-constrained devices in mind, IDGP provides a guide for those wishing to implement genetic programming on such systems. Furthermore, an implementation on mote class devices is demonstrated to evolve logic for a time-varying sense-compute-act problem and another problem requiring the evolution of primitive communications. Distributed evolution of logic is also achieved by employing the Island Model architecture, and a comparison of individual and distributed evolution (with the same and iii iv Abstract slightly different goals) presented. This demonstrates the advantage of leveraging the fact that such devices often reside within networks of devices experiencing similar conditions.Since GP is a population-based metaheuristic which relies on the diversity of the population to achieve learning, many, if not most, programs within the population exhibit poor performance. As such, the average observed performance (pool fitness) of the population using the standard GP learning mechanism is unlikely to be acceptable for online learning scenarios. This is suspected to be the reason why no previous attempts have been made to deploy standard GP as an online learning approach. Nonetheless, the benefits of GP for evolving logic on such devices are compelling and motivated the design of a novel satisficing heuristic called Fitness Importance (FI). FI is population-based heuristic used to bias the evaluation of candidate solutions such that an "acceptable" average fitness (AAF) is achieved while also achieving ongoing, though diminished, learning capacity. This trade off motivated further investigation into whether dynamically adjusting the average performance in response to AAF would be superior to a constant, balanced, performing-learning approach. Dynamic and constant strategies were com...

show abstract

Benefits of a Population: Five Mechanisms That Advantage Population-Based Algorithms

Cited by 72 publications

References 26 publications

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

Scalable Hierarchical Clustering: Twister Tries with a Posteriori Trie Elimination

A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting

In situ Distributed Genetic Programming: An Online Learning Framework for Resource Constrained Networked Devices

Contact Info

Product

Resources

About