Evolutionary computation: the next major transition of artificial intelligence?

Sipper, Moshe; Olson, Randal S.; Moore, Jason H.

doi:10.1186/s13040-017-0147-3

Cited by 24 publications

(16 citation statements)

References 10 publications

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“…Well-known methods such as bagging [ 1 ], boosting [ 2 ], and stacking [ 3 ] are ML mainstays, widely (and fruitfully) deployed on a daily basis. Generally speaking, there are two types of ensemble methods, the first generating models in sequence—e.g., AdaBoost [ 2 ]—the latter in a parallel manner—e.g., random forests [ 4 ] and evolutionary algorithms [ 5 ].…”

Section: Editorialmentioning

confidence: 99%

Conservation machine learning

Sipper¹,

Moore²

2020

BioData Mining

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Ensemble techniques-wherein a model is composed of multiple (possibly) weaker models-are prevalent nowadays within the field of machine learning (ML). Well-known methods such as bagging [1], boosting [2], and stacking [3] are ML mainstays, widely (and fruitfully) deployed on a daily basis. Generally speaking, there are two types of ensemble methods, the first generating models in sequence-e.g., AdaBoost [2]-the latter in a parallel manner-e.g., random forests [4] and evolutionary algorithms [5]. AdaBoost (Adaptive Boosting) is an ML meta-algorithm that is used in conjunction with other types of learning algorithms to improve performance. The output of so-called "weak learners" is combined into a weighted sum that represents the final output of the boosted classifier. Adaptivity is obtained by tweaking subsequent weak learners in favor of those instances misclassified by previous classifiers. The maximum number of estimators at which boosting is terminated is a free parameter that has to be carefully set by the user. The popular Scikit-learn Python package, used extensively within the ML community, sets this default value to 50 [6]. A random forest is an ensemble learning method that operates by constructing a multitude of decision trees at training time and then outputting the majority class (for classification problems) or mean prediction (for regression problems) of the individual trees. The number of trees is a free parameter set by the user; the default Scikit-learn value is 100 (up from 10 in past versions) [6]. An evolutionary algorithm is a population-based approach that inherently produces a cornucopia of models over generations of evolution. Most often one seeks a single, final model (or a Pareto set of models, when multiple objectives are sought). Yet, as eloquently suggested by [7] in their paper's title, might we not obtain "Ensemble learning for free with evolutionary algorithms?" They proposed evolutionary ensemble learning, which extracts an ensemble either from the final population only or incrementally during evolution. Recently, [8] focused on genetic programming-wherein the individuals evolved are computational trees-introducing an ensemble coevolutionary algorithm that maintains two subpopulations, trees and forests, with the output model being a forest built as an ensemble of trees.

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Section: Editorialmentioning

confidence: 99%

Conservation machine learning

Sipper¹,

Moore²

2020

BioData Mining

Self Cite

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“…WOA operates well on a single machine and has proven to outperform another Evolutionary Algorithm (EA) [36,[39][40][41][42][43]. However, as mentioned earlier, the WOA faces the major drawback against low convergence speed when it comes to complicated issues.…”

Section: Cluster Based Computation For Woamentioning

confidence: 99%

Methodology for Modified Whale Optimization Algorithm for Solving Appliances Scheduling Problem

Omar

Bakeri

Nawi

et al. 2020

ARFMTS

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Whale Optimization Algorithm (WOA) is considered as one of the newest metaheuristic algorithms to be used for solving a type of NP-hard problems. WOA is known of having slow convergence and at the same time, the computation of the algorithm will also be increased exponentially with multiple objectives and huge request from n users. The current constraints surely limit for solving and optimizing the quality of Demand Side Management (DSM) case, such as the energy consumption of indoor comfort index parameters which consist of thermal comfort, air quality, humidity and vision comfort. To address these issues, this proposed work will firstly justify and validate the constraints related to the appliances scheduling problem, and later proposes a new model of the Cluster based Multi-Objective WOA with multiple restart strategy. In order to achieve the objectives, different initialization strategy and cluster-based approaches will be used for tuning the main parameter of WOA under different MapReduce application which helps to control exploration and exploitation, and the proposed model will be tested on a set of well-known test functions and finally, will be applied on a real case project i.e. appliances scheduling problem. It is anticipating that the approach can expedite the convergence of meta-heuristic technique with quality solution.

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“…Swarm intelligence algorithms are non-linear, self-organized and scalable which used to understand the collective behavior of the users to determine the nearest neighbors and performs clustering in a dynamic environment. Evolution computing 39 has defined from concepts of evolutionary biology used to optimization problems and continuous optimization. There are several methodologies are available to tackle the optimization issues namely genetic algorithms, evolutionary programming and strategies.…”

Section: Artificial Intelligence Based Rsmentioning

confidence: 99%

A SURVEY ON COMPREHENSIVE TRENDS IN RECOMMENDATION SYSTEMS & APPLICATIONS

Addagarla

Amalanathan

2019

IJECS

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Recommendation System (RS) gains considerable popularity from the past decade in E-Commerce and other allied fields. This paper investigates the various traditional Recommendation System like Content-based (CB), Collaboration Filtering-based (CF), Demographic-based, Knowledge-based and discussed current trends in recommendation system like location-aware, context-aware, and Deep Learning techniques. Various improvements and limitations in Recommendation systems have been listed out with evolution metrics for analyzing the accuracy of the algorithms. This paper wellelaborated for the past, present and future scope of the Recommendation System which would be useful for researchers to get familiarity with this domain.

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Evolutionary computation: the next major transition of artificial intelligence?

Cited by 24 publications

References 10 publications

Conservation machine learning

Conservation machine learning

Methodology for Modified Whale Optimization Algorithm for Solving Appliances Scheduling Problem

A SURVEY ON COMPREHENSIVE TRENDS IN RECOMMENDATION SYSTEMS & APPLICATIONS

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