Evolutionary Algorithms in Health Technologies

Ling, Sai Ho; Lam, Hak‐Keung

doi:10.3390/a12100202

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…First, they allow us to examine what conditions allow learning and evolution to reach an optimal solution and what conditions lead to heuristic decision strategies (Kvam et al, 2019). On the other, simulating evolutionary processes allows us to discover new decision strategies or mechanisms that we might not otherwise have considered as candidate explanations for behavior (Ling & Lam, 2019;Miikkulainen, 2021;Miikkulainen et al, 2019;Lehman et al, 2018). We refer to these approaches as computational evolution.…”

Section: Model Testing and Discovery: Computational Evolutionmentioning

confidence: 99%

Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior

Kvam¹,

Sokratous²,

Fitch³

et al. 2023

Preprint

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Theories of decision making are implemented in models that predict and explain behavior in terms of latent cognitive processes. But where do these models come from, and how are they instantiated in the brain? In this paper, we examine several avenues where artificial intelligence (AI) and machine learning (ML) can benefit decision theory by providing new methods for developing and testing cognitive models. First, machine learning can be used to efficiently estimate the values of latent parameters in cognitive models, and assign posterior probabilities to competing models of the same observed data. Second, models of decision behavior can be embedded within artificially intelligent systems to allow them to make inferences about human counterparts (goals, abilities, cognition) in real time, equipping AI better tools to interact socially. Third, AI can be used to understand how evolutionary and learning processes give rise to the cognitive abilities that support decision-making. Finally, the tools of experimental psychology and decision sciences can be applied to better understand the ``black boxes'' of neural networks by systematically testing input-output (stimulus-response) relationships. Put together, we suggest that merging ML/AI into decision modeling -- and vice versa -- is a promising path toward many long-term benefits for both fields.

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Section: Model Testing and Discovery: Computational Evolutionmentioning

confidence: 99%

Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior

Kvam¹,

Sokratous²,

Fitch³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In information retrieval systems, genetic algorithms are often employed to improve the process of retrieving information. Evolutionary algorithms were used in text data acquisition by Ling and Lam [16]. In the years that followed, the author developed a number of text clustering approaches that aided in the advancement of text clustering techniques.…”

Section: Measurement Of User Experiencementioning

confidence: 99%

Machine Learning and AI Application Behaviour Prediction for User Experience Modelling and Optimization

Neilson

Grigore

2022

JCNS

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The purpose of this research is to offer a technique for assessing user experience in mobile applications utilizing AIAM technology. Due to ineffective and time-consuming nature of conventional data gathering techniques (such as user interviews and user inference), AIAM concentrates on using Artificial Intelligence (AI) to assess and enhance user experience. Logs from a mobile application may be used to gather information about user activity. Only a few parameters of data are utilized in the process of surfing and running mobile applications to ensure the privacy of users. The method's objective is to create the deep neural network prototype as close as feasible to a user's experience when using a mobile app. For particular objectives, we create and employ application interfaces to train computational models. The click data from all users participating in a certain task is shown on these projected pages. User activity may therefore be mapped in connected and hidden layers of the system. Finally, the social communications application is used to test the efficacy of the suggested method by implementing the improved design.

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“…A S THE basic idea of evolutionary algorithms (EA) [1]- [3] is to copy the natural process of evolution [4] many characteristics of biological evolution are already considered [5] and implemented in EA comparable to the biological processes. With these mechanisms all kind of problems have been tackled in the field of general engineering [6]- [8], turbulence modeling [9]- [13], or health technologies [14]. Additionally, EAs have even been successfully used in arts and music [15]- [17] or software engineering [18]- [20] to fix bugs or to increase the performance of compilers.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Way of Introducing Gender Into Evolutionary Algorithms

Kasten

Fahr

Klein

2023

IEEE Trans. Evol. Computat.

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Evolutionary algorithms have been extensively used for numerous optimization problems with great success in the past years. They mimic nature's process of evolution to find solutions to a large range of mathematical problems. In this work a new strategy is suggested to introduce gender, defined by a characteristic of an individual, that is easy to implement in evolutionary algorithms, as long as they are based on evaluating a fitness function. The new method outperforms comparable evolutionary approaches without gender for all standard test problems considered. The present study shows that with increasing problem complexity the performance of this gender variant increases to more than double the success rates while keeping the computational effort at about the same level and still being clearly better for easy problems. Additionally, in the mean, the new method results in better fitness values for all presented cases.

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Evolutionary Algorithms in Health Technologies

Cited by 4 publications

References 6 publications

Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior

Using artificial intelligence to fit, compare, evaluate, and discover computational models of decision behavior

Machine Learning and AI Application Behaviour Prediction for User Experience Modelling and Optimization

An Efficient Way of Introducing Gender Into Evolutionary Algorithms

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