Is Evolutionary Computation Evolving Fast Enough?

Kendall, Graham

doi:10.1109/mci.2018.2807019

Cited by 7 publications

(3 citation statements)

References 114 publications

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“…Nevertheless, challenging problems of multisensory data fusion algorithms are still far from accomplished [ 6 ]. Evolutionary computation methods [ 7 ] recently seem to be making a comeback in order to solve real-world problems concerning typically not iid (independent and identically distributed) data or sparse labeled data, and these methods are expected to help such a fusion model enhanced.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Kim

Suh

2018

Sensors

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A hybrid particle swarm optimization (PSO), able to overcome the large-scale nonlinearity or heavily correlation in the data fusion model of multiple sensing information, is proposed in this paper. In recent smart convergence technology, multiple similar and/or dissimilar sensors are widely used to support precisely sensing information from different perspectives, and these are integrated with data fusion algorithms to get synergistic effects. However, the construction of the data fusion model is not trivial because of difficulties to meet under the restricted conditions of a multi-sensor system such as its limited options for deploying sensors and nonlinear characteristics, or correlation errors of multiple sensors. This paper presents a hybrid PSO to facilitate the construction of robust data fusion model based on neural network while ensuring the balance between exploration and exploitation. The performance of the proposed model was evaluated by benchmarks composed of representative datasets. The well-optimized data fusion model is expected to provide an enhancement in the synergistic accuracy.

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Section: Introductionmentioning

confidence: 99%

Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Kim

Suh

2018

Sensors

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“…As far as we know, addressing the gradually deepening self-adaptive and self-autonomous requirement to maintain the trustworthiness of WfMS, hyperparameters, machine learning, and search techniques [44], will play an important role in such a scenario described in Section 2. However, just as with the position the paper critically evaluated, genetic programming and hyper-heuristics should be embedded into the framework of large scale software development [45], e.g., TWfMS. Hence, due to there is no free lunch theorems for optimization [46], not only do we need traditional knowledge discovery in a database (KDD) process for extracting useful knowledge from Big Data [47], but we should also investigate the key technologies with interoperable interface specification standardization for developing TWfMSs.…”

Section: Discussionmentioning

confidence: 99%

Hierarchical Information Entropy System Model for TWfMS

Han

Yang

2018

Entropy

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Under the infrastructure of three gradually deepening layers consisting of System, Service and Software, the information entropy of the Trustworthy Workflow Management System (TWfMS) will evolve from being more precise to more undetermined, due to a series of exception event X occurring on certain components (ExCs), along with the life cycle of TWfMS, experienced in its phased original, as-is, to-be, and agile-consistent stages, and recover, more precisely again, by turning back to the original state from the agile-consistent stage, due to its self-autonomous improvement. With a special emphasis on the system layer, to assure the trustworthiness of WfMS, this paper firstly introduces the preliminary knowledge of the hierarchical information entropy model with correlation theories. After illustrating the fundamental principle, the transformation rule is deduced, step by step, followed by a case study, which is conducive to generating discussions and conclusions in the different research areas of TWfMS. Overall, in this paper, we argue that the trustworthiness maintenance of WfMS could be analyzed and computational, through the viewpoint that all the various states of TWfMS can be considered as the transformation between WfMS and its trustworthiness compensate components, whose information entropy fluctuate repeatedly and comply with the law of the dissipative structure system.

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“…( 1) has led to immense interest among scientists and engineers for decades, resulting in the development of a plethora of computational techniques for tackling them. While there exists a sea of associated algorithms to choose from, the interest of the present paper lies in a family of natureinspired optimization methodologies that make up the field of evolutionary computation [1,2]. As the name suggests, the algorithms belonging to this fieldreferred to as genetic algorithms or evolutionary algorithms (GAs / EAs for short)draw inspiration from the foundational principles of evolutionary biology as laid out by Charles Darwin in his 1859 book "On the Origin of Species" [3].…”

Section: Introductionmentioning

confidence: 99%

Back to the Roots: Multi-X Evolutionary Computation

Gupta

Ong

2019

Cogn Comput

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BACKGROUND: Over the years, evolutionary computation has come to be recognized as one of the leading algorithmic paradigms in the arena of global black-box optimization. The distinguishing facets of evolutionary methods, inspired by Darwin's foundational principles of natural selection, stem mainly from their population-based search strategywhich gives rise to the phenomenon of implicit parallelism. Precisely, even as an evolutionary algorithm manipulates a population of a few candidate solutions (or: individuals), it is able to simultaneously sample, evaluate, and process a vast number of regions of the search space. This behavior is in effect analogous to our inherent cognitive ability of processing diverse information streams (such as sight and sound) with apparent simultaneity in different regions of our brain. METHODS:For this reason, evolutionary algorithms have emerged as the method of choice for those search and optimization problems where a collection of multiple target solutions (that may be scattered throughout the search space) are to be found in a single run. With the above in mind, in this paper we return to the roots of evolutionary computation, with the aim of shedding light on a variety of problem settings that are uniquely suited for exploiting the implicit parallelism of evolutionary algorithms. RESULTS:Our discussions cover established concepts of multi-objective and multi-modal optimization, as well as new (schema) theories pertaining to emerging problem formulations that entail multiple searches to be carried out at once. We capture associated research activities under the umbrella term of multi-X evolutionary computation, where X, as of now, represents the following list: {'objective ', 'modal', 'task', 'level', 'hard', 'disciplinary', 'form'}. CONCLUSIONS: With this, we hope that the present position paper will serve as a catalyst for effecting further research efforts into such areas of optimization problem-solving that are well-aligned with the fundamentals of evolutionary computation; in turn prompting the steady update of the list X with new applications in the future.

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Is Evolutionary Computation Evolving Fast Enough?

Cited by 7 publications

References 114 publications

Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Hybrid Particle Swarm Optimization for Multi-Sensor Data Fusion

Hierarchical Information Entropy System Model for TWfMS

Back to the Roots: Multi-X Evolutionary Computation

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