Genetic Programming

Poli, Riccardo; Koza, John R.

doi:10.1007/978-1-4614-6940-7_6

Cited by 40 publications

(37 citation statements)

References 76 publications

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“…Optimization includes numerical analysis focused on problem solving in various Big Data challenges: volume, velocity, variety and veracity [62] that will be discussed in more detail later. Some widely used analytical techniques are genetic programming [63][64][65], evolutionary programming [66] and particle swarm optimization [67,68]. Optimization is focused on the search of the optimal set of actions needed to improve system performance.…”

Section: Big Data Processing Methodsmentioning

confidence: 99%

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Olshannikova

Ometov

Koucheryavy

et al. 2015

Journal of Big Data

251

121

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Section: Big Data Processing Methodsmentioning

confidence: 99%

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Olshannikova

Ometov

Koucheryavy

et al. 2015

Journal of Big Data

251

121

View full text Add to dashboard Cite

“…VFD is based on GP, details about which can be found in the books [5], [6], and Section III contains a short description as well. Other members of the EA family are described in various textbooks, such as [5] and [7].…”

Section: Related Workmentioning

confidence: 99%

Value Function Discovery in Markov Decision Processes With Evolutionary Algorithms

Onderwater

Bhulai

Mei

2016

IEEE Trans. Syst. Man Cybern, Syst.

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Abstract-In this paper we introduce a novel method for discovery of value functions for Markov Decision Processes (MDPs). This method, which we call Value Function Discovery (VFD), is based on ideas from the Evolutionary Algorithm field. VFD's key feature is that it discovers descriptions of value functions that are algebraic in nature. This feature is unique, because the descriptions include the model parameters of the MDP. The algebraic expression of the value function discovered by VFD can be used in several scenarios, e.g., conversion to a policy (with one-step policy improvement) or control of systems with time-varying parameters. The work in this paper is a first step towards exploring potential usage scenarios of discovered value functions. We give a detailed description of VFD and illustrate its application on an example MDP. For this MDP we let VFD discover an algebraic description of a value function that closely resembles the optimal value function. The discovered value function is then used to obtain a policy, which we compare numerically to the optimal policy of the MDP. The resulting policy shows near-optimal performance on a wide range of model parameters. Finally, we identify and discuss future application scenarios of discovered value functions.

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“…Multi-objective optimization finds two or more optimal solutions optimizing many objective functions at the same time, while many optimal solutions derive from the objective space. Optimal solutions are often visualized, by forming a Pareto front aiding an enhanced decision making process [2].…”

Section: Introductionmentioning

confidence: 99%

A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization

et al. 2021

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Over the past few decades, industry and academia have made great strides to improve aspects related with optimal energy management. These include better ways for efficient energy asset management, generating great opportunities for optimization of energy distribution, discomfort minimization, energy production, cost reduction and more. This paper proposes a framework for a multi-objective analysis, acting as a novel tool that offers responses for optimal energy management through a decision support system. The novelty is in the structure of the methodology, since it considers two distinct optimization problems for two actors, consumers and aggregators, with solution being able to completely or partly interact with the other one is in the form of a demand response signal exchange. The overall optimization is formulated by a bi-objective optimization problem for the consumer side, aiming at cost minimization and discomfort reduction, and a single objective optimization problem for the aggregator side aiming at cost minimization. The framework consists of three architectural layers, namely, the consumer, aggregator and decision support system (DSS), forming a tri-layer optimization framework with multiple interacting objects, such as objective functions, variables, constants and constraints. The DSS layer is responsible for decision support by forecasting the day-ahead energy management requirements. The main purpose of this study is to achieve optimal management of energy resources, considering both aggregator and consumer preferences and goals, whilst abiding with real-world system constraints. This is conducted through detailed simulations using real data from a pilot, that is part of Terni Distribution System portfolio.

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Genetic Programming

Cited by 40 publications

References 76 publications

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Visualizing Big Data with augmented and virtual reality: challenges and research agenda

Value Function Discovery in Markov Decision Processes With Evolutionary Algorithms

A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization

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