Population size influence on the energy consumption of genetic programming

Álvarez, Josefa Díaz; Castillo, Pedro; Vega, Francisco Fernández de; Chávez, Francisco; Alvarado, Jorge L.

doi:10.1177/00202940211064471

Cited by 6 publications

(3 citation statements)

References 39 publications

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“…From the algorithmic perspective, a number of papers dealing with energy improvement have already been published [43], [44], [19], but these are the first exploratory papers on the ideas that may be applied to use energy consumption as a new optimization goal: the quality of the solutions is no longer the only relevant criterion, but it needs to be balanced with the energy consumption in mind, both during the training or optimization, and also when the solution itself is deployed massively for thousands or millions of users.…”

Section: E Software Optimization: Programming Languages Algorithms De...mentioning

confidence: 99%

“…In any case, it is crucial to see how the algorithm behaves from the energy consumption point of view, so power consumption measures must be taken similarly to running times. As described in the literature review section, some interesting issues have been raised on the influence of some parameters on the energy consumption behavior of particular algorithms [44], so we foresee the same possibilities for future algorithms still to be conceived.…”

Section: B Energy Efficient Measures Of Interest At Different Levels ...mentioning

confidence: 99%

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Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Hidalgo,

Fenández-de_Vega,

Ceberio

et al. 2023

Preprint

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The energy consumption of Artificial Intelligence (AI) systems has increased 300,000-fold from 2012 to now, and data centers running massive AI software produce up to 5-9% of global electricity demand and 2% of all CO2 emissions. Such an increase in energy consumption has been partially motivated by the strong development of new AI-specific architectures to improve the performance of AI models. Nevertheless, the AI community has recently become aware of the importance of considering energy efficiency as a metric when developing AI techniques. To date, a great effort has been made to find optimal AI model configurations that provide the best solution in the shortest possible time. However, only a few works have sought a compromise between energy cost and system performance. This paper analyses recent efforts in these directions and proposes the path toward energy-efficient AI. We describe a set of energy efficiency strategies for applying and deploying AI models on different computing infrastructures in search of democratizing an environmentally sustainable AI. To that end, we propose a full-stack approach of energy-efficient AI and analyze the role that different types of users should play, tackling the energy-focused optimization in all steps of the AI model design flow, from the high levels of models and algorithm design to the low levels ones, more related to the hardware and architecture.

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Section: E Software Optimization: Programming Languages Algorithms De...mentioning

confidence: 99%

Section: B Energy Efficient Measures Of Interest At Different Levels ...mentioning

confidence: 99%

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Hidalgo,

Fenández-de_Vega,

Ceberio

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Previous studies have investigated the impact of this parameter on the performance of evolutionary algorithms [198,211,195]. In general, it can be concluded that the effect of the population on the performance of EC algorithms is complicated [65,201,222]. On the one hand, if the population size is small, the search capability of GP will be restricted and hence, the performance will be impacted negatively.…”

Section: The Effect Of Population Sizementioning

confidence: 99%

Transfer Optimisation in Genetic Programming for Solving Uncertain Capacitated Arc Routing Problem

Ardeh¹

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Uncertain Capacitated Arc Routing Problem (UCARP) is a combinatorial optimisation problem with many important real-world applications. Genetic programming (GP) is a powerful machine learning technique that has been successfully used to automatically evolve routing policies for UCARP. Reusability is an open issue in the field of UCARP and in this direction, an open challenge is the case of scenario changes, e.g. change in the number of vehicles and probability distributions of random demands, which typically requires new training procedures to be initiated. Considering the expensive training cost of evolving routing policies for UCARP, a promising strategy is to learn and reuse knowledge from a previous problem-solving process to improve the effectiveness and efficiency of solving a new related problem, i.e. transfer learning. The overall goal of this thesis is to develop novel knowledge transfer algorithms for GP for solving UCARP to handle environment changes more effectively and efficiently. To fulfil this goal, a plethora of machine learning techniques, i.e. surrogate models, feature selection, searching and specialised genetic operators, are utilised in this thesis. First, this thesis explores the effectiveness of the existing transfer optimisation methods for solving UCARP. Accordingly, one of the main directions of this thesis is towards identifying the nature of transferable knowledge, which can impact the quality of knowledge transfer for GP to solve UCARP. For this purpose, a collection of the state-of-the-art transfer optimisation GP algorithms are evaluated for UCARP. After identifying some potential gaps in the literature, a number of preliminary transfer optimisation algorithms are proposed that supplement the literature. To evaluate the algorithms, a large set of knowledge transfer scenarios with various source and target problems were designed based on real-world datasets. According to the results, none of the methods showed significant improvement in the effectiveness of the trained UCARP routing policies. These results revealed the need for more effective transfer optimisation methods specifically designed for UCARP. Furthermore, our investigations revealed that the presence of duplicates in knowledge sources is one of the main challenges for effective transfer optimisation in solving UCARP. Second, we propose approaches to handling the presence of duplicates in the transferred knowledge. The first approach increases population diversity after knowledge transfer to counteract the loss of diversity that is introduced by the presence of duplicates in the transferred knowledge. In the second approach, the duplicates are removed from the transferred knowledge. Then, the transferred knowledge is utilised to create a diverse initial GP population of high-quality individuals. Both approaches are investigated through detailed experimental studies. The results indicate that, while the first approach did not perform better than GP with knowledge transfer, the second can improve the effectiveness of training routing policies with GP significantly. Third, this thesis proposes a novel algorithm that transfers the phenotypic characteristics of the routing policies for solving the source problem. In the new algorithm, the most fit and unique source routing policies are utilised for initialising GP for solving the target problem. Then, a tabu list is placed on the source routing policies and the GP process is prohibited from recreating any of the source routing policies. The motivation for this approach is that, due to the existence of similarity between the source and target problems, source routing policies are unlikely to have a good performance for the target problem. Our experimental studies confirmed that by prohibiting GP from recreating source policies, and the computational resources will be spent on searching and evaluating new regions of the search space, which can lead to discovering better solutions. Fourth, this thesis proposes a novel knowledge transfer algorithm based on the idea of maintaining the transferred knowledge as an auxiliary population. In this approach, first, the best individuals of the duplicate-free knowledge source are used to initialise GP. Additionally, these transferred individuals are also maintained as an auxiliary population and are evolved alongside the main population. To save the computational cost, the auxiliary population is evolved with a surrogate method. Additionally, an elaborate knowledge exchange mechanism between the two populations is devised that emphasises transferring high-quality and unique individuals, the transfer of which can improve the diversity of the receiving population. This allows GP to overcome the problem of losing its population diversity during the evolutionary process. Our detailed experimental results confirmed the superior performance of the proposed algorithm and confirmed that the proposed method improved the phenotypic diversity of GP population.

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A systematic review of Green AI

Verdecchia

Sallou

Cruz

2023

WIREs Data Min & Knowl

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With the ever‐growing adoption of artificial intelligence (AI)‐based systems, the carbon footprint of AI is no longer negligible. AI researchers and practitioners are therefore urged to hold themselves accountable for the carbon emissions of the AI models they design and use. This led in recent years to the appearance of researches tackling AI environmental sustainability, a field referred to as Green AI. Despite the rapid growth of interest in the topic, a comprehensive overview of Green AI research is to date still missing. To address this gap, in this article, we present a systematic review of the Green AI literature. From the analysis of 98 primary studies, different patterns emerge. The topic experienced a considerable growth from 2020 onward. Most studies consider monitoring AI model footprint, tuning hyperparameters to improve model sustainability, or benchmarking models. A mix of position papers, observational studies, and solution papers are present. Most papers focus on the training phase, are algorithm‐agnostic or study neural networks, and use image data. Laboratory experiments are the most common research strategy. Reported Green AI energy savings go up to 115%, with savings over 50% being rather common. Industrial parties are involved in Green AI studies, albeit most target academic readers. Green AI tool provisioning is scarce. As a conclusion, the Green AI research field results to have reached a considerable level of maturity. Therefore, from this review emerges that the time is suitable to adopt other Green AI research strategies, and port the numerous promising academic results to industrial practice. This article is categorized under: Technologies > Machine Learning

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Population size influence on the energy consumption of genetic programming

Cited by 6 publications

References 39 publications

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Sustainable Artificial Intelligence Systems: An Energy Efficiency Approach

Transfer Optimisation in Genetic Programming for Solving Uncertain Capacitated Arc Routing Problem

A systematic review of Green AI

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