Environmental Optimization: Applications of Genetic Algorithms

Haupt, Sue Ellen

doi:10.1007/978-1-4020-9119-3_18

“…Genetic Algorithms (GA) are based on the process of evolution in natural systems in that a population of competing solutions evolves over time to converge on an optimal solution (Holland 1975;Goldberg & Holland 1988;Olden et al 2008;Koza 1992;Haupt & Haupt 2004). Solutions are represented as "chromosomes" and model parameters are represented as "genes" on those chromosomes (Fig.…”

Section: Genetic Algorithmsmentioning

confidence: 99%

Adoption of machine learning techniques in Ecology and Earth Science

Thessen

¹

2016

Preprint

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The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

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“…GAs have seen a rise in popularity due to development of the Genetic Algorithm for Rule-Set Prediction (GARP) used to predict species distributions (Stockwell & Noble 1992). GAs are very popular in hydrology (see Mulligan & Brown 1998 for description of how GA was used to find the Pareto Front) and meteorology (Haupt 2009). GAs are able to cope with uneven sampling and small sample sizes (Olden et al 2008).…”

Section: Genetic Algorithmsmentioning

confidence: 99%

“…Sometimes remote sensing data and the parameters inferred from them can require spatial interpolation in the vertical or horizontal dimension, which is often performed using ML methods (Li et al 2011;Krasnopolsky 2009). Common tools for classifying remote sensing images include Random Forest Duro et al 2012), support vector machines (Durbha et al 2007;Zhao et al 2011;Duro et al 2012), neural networks (Rogan et al 2008), genetic algorithms (Haupt 2009), and decision trees (Huang & Jensen 1997).…”

Section: Remote Sensingmentioning

confidence: 99%

“…ML methods can provide a means of increasing certainty and improving results, despite data gaps. Several algorithms have been applied to water (Maier & Dandy 2000;Haupt 2009), soil (Henderson et al 2005;Tscherko et al 2007), and biodiversity/wildlife management (Baran et al 1996;Lek, Delacoste, et al 1996;Lek, Belaud, et al 1996;Giske et al 1998;Guégan et al 1998;Spitz & Lek 1999;Chen et al 2000;Vander Zanden et al 2004;Sarkar et al 2006;Worner & Gevrey 2006;Cutler et al 2007;Quintero et al 2014;Bland et al 2014;Jones et al 2006). ML methods have been used to model population dynamics, production, and biomass in terrestrial, aquatic, marine, and agricultural systems (Scardi 1996;Recknagel 1997;Scardi & Harding Jr. 1999;Recknagel et al 2000;McKenna Jr. 2005;Muttil & Lee 2005;Recknagel et al 2002;Džeroski 2001;Schultz et al 2000).…”

Section: Resource Managementmentioning

confidence: 99%

“…Being able to make these types of predictions and inferences can help focus conservation efforts for maximum impact Guisan et al 2013). Common ML methods for resource management include genetic algorithms (Haupt 2009), neural networks (Brey et al 1996;Kastens & Featherstone 1996;Recknagel 1997;Giske et al 1998;Guégan et al 1998;Schultz et al 2000;Lee et al 20007), support vector machines (Guo et al 2005;Drake et al 2006), fuzzy logic (Tscherko et al 2007), decision trees (Henderson et al 2005;Jones et al 2006), and Random Forest (Cutler et al 2007;Quintero et al 2014;Furlanello et al 2003).…”

Section: Resource Managementmentioning

confidence: 99%

See 2 more Smart Citations

Adoption of machine learning techniques in Ecology and Earth Science

Thessen

¹

2016

Preprint

View full text Add to dashboard Cite

The natural sciences, such as ecology and earth science, study complex interactions between biotic and abiotic systems in order to infer understanding and make predictions. Machine-learning-based methods have an advantage over traditional statistical methods in studying these systems because the former do not impose unrealistic assumptions (such as linearity), are capable of inferring missing data, and can reduce long-term expert annotation burden. Thus, a wider adoption of machine learning methods in ecology and earth science has the potential to greatly accelerate the pace and quality of science. Despite these advantages, machine learning techniques have not had wide spread adoption in ecology and earth science. This is largely due to 1) a lack of communication and collaboration between the machine learning research community and natural scientists, 2) a lack of easily accessible tools and services, and 3) the requirement for a robust training and test data set. These impediments can be overcome through financial support for collaborative work and the development of tools and services facilitating ML use. Natural scientists who have not yet used machine learning methods can be introduced to these techniques through Random Forest, a method that is easy to implement and performs well. This manuscript will 1) briefly describe several popular ML methods and their application to ecology and earth science, 2) discuss why ML methods are underutilized in natural science, and 3) propose solutions for barriers preventing wider ML adoption.

show abstract

A scalable species-based genetic algorithm for reinforcement learning problems

Seth

¹

,

Νίκου

²

,

Daoutis

³

2022

The Knowledge Engineering Review

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Reinforcement Learning (RL) methods often rely on gradient estimates to learn an optimal policy for control problems. These expensive computations result in long training times, a poor rate of convergence, and sample inefficiency when applied to real-world problems with a large state and action space. Evolutionary Computation (EC)-based techniques offer a gradient-free apparatus to train a deep neural network for RL problems. In this work, we leverage the benefits of EC and propose a novel variant of genetic algorithm called SP-GA which utilizes a species-inspired weight initialization strategy and trains a population of deep neural networks, each estimating the Q-function for the RL problem. Efficient encoding of a neural network that utilizes less memory is also proposed which provides an intuitive mechanism to apply Gaussian mutations and single-point crossover. The results on Atari 2600 games outline comparable performance with gradient-based algorithms like Deep Q-Network (DQN), Asynchronous Advantage Actor Critic (A3C), and gradient-free algorithms like Evolution Strategy (ES) and simple Genetic Algorithm (GA) while requiring far fewer hyperparameters to train. The algorithm also improved certain Key Performance Indicators (KPIs) when applied to a Remote Electrical Tilt (RET) optimization task in the telecommunication domain.

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Environmental Optimization: Applications of Genetic Algorithms

Cited by 4 publications

References 48 publications

Adoption of machine learning techniques in Ecology and Earth Science

Adoption of machine learning techniques in Ecology and Earth Science

Adoption of machine learning techniques in Ecology and Earth Science

A scalable species-based genetic algorithm for reinforcement learning problems

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