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- Title
- A differential evolution approach to feature selection in genomic prediction
- Creator
- Whalen, Ian
- Date
- 2018
- Collection
- Electronic Theses & Dissertations
- Description
-
The use of genetic markers has become widespread for prediction of genetic merit in agricultural applications and is a beginning to show promise for estimating propensity to disease in human medicine. This process is known as genomic prediction and attempts to model the mapping between an organism's genotype and phenotype. In practice, this process presents a challenging problem. Sequencing and recording phenotypic traits are often expensive and time consuming. This leads to datasets often...
Show moreThe use of genetic markers has become widespread for prediction of genetic merit in agricultural applications and is a beginning to show promise for estimating propensity to disease in human medicine. This process is known as genomic prediction and attempts to model the mapping between an organism's genotype and phenotype. In practice, this process presents a challenging problem. Sequencing and recording phenotypic traits are often expensive and time consuming. This leads to datasets often having many more features than samples. Common models for genomic prediction often fall victim to overfitting due to the curse of dimensionality. In this domain, only a fraction of the markers that are present significantly affect a particular trait. Models that fit to non-informative markers are in effect fitting to statistical noise, leading to a decrease in predictive performance. Therefore, feature selection is desirable to remove markers that do not appear to have a significant effect on the trait being predicted. The method presented here uses differential evolution based search for feature selection. This study will characterize differential evolution's efficacy in feature selection for genomic prediction and present several extensions to the base search algorithm in an attempt to apply domain knowledge to guide the search toward better solutions.
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- Title
- Evolution of cooperation in the light of information theory
- Creator
- Mirmomeni, Masoud
- Date
- 2015
- Collection
- Electronic Theses & Dissertations
- Description
-
Cooperation is ubiquitous in different biological levels and is necessary for evolution to shape the life and create new forms of organization. Genes cooperate in controlling cells; cells efficiently collaborate together to produce cohesive multi-cellular organisms; members of insect colonies and animal clans cooperate in protecting the colony and providing food. Cooperation means that members of a group bear a cost, c, for another individuals to earn a benefit, b. While cooperators of the...
Show moreCooperation is ubiquitous in different biological levels and is necessary for evolution to shape the life and create new forms of organization. Genes cooperate in controlling cells; cells efficiently collaborate together to produce cohesive multi-cellular organisms; members of insect colonies and animal clans cooperate in protecting the colony and providing food. Cooperation means that members of a group bear a cost, c, for another individuals to earn a benefit, b. While cooperators of the group help others by paying a cost, defectors receive the benefits of this altruistic behavior without providing any service in return to the group. To address this dilemma, here we use a game theoretic approach to model and study evolutionary dynamics that can lead to unselfish behavior. Evolutionary game theory is an approach to study frequency-dependent systems. In evolutionary games the fitness of individuals depends on the relative abundance of the various types in the population. We explore different strategies and different games such as iterated games between players with conditional strategies, multi player games, and iterated games between fully stochastic strategies in noisy environments to find the necessity conditions that lead to cooperation. Interestingly, we see that in all of these games communication is the key factor for maintaining cooperation among selfish individuals. We show that communication and information exchange is necessary for the emergence of costly altruism, and to maintain cooperation in the group there should be minimum rate of communication between individuals. We quantify this minimum amount of information exchange, which is necessary for individuals to exhibit cooperative behavior, by defining a noisy communication channel between them in iterated stochastic games and measuring the communication rate (in bits) during the break down of cooperation.
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- Title
- Evolution of distributed behavior
- Creator
- Knoester, David B.
- Date
- 2011
- Collection
- Electronic Theses & Dissertations
- Description
-
In this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of...
Show moreIn this dissertation, we describe a study in the evolution of distributed behavior, where evolutionary algorithms are used to discover behaviors for distributed computing systems. We define distributed behavior as that in which groups of individuals must both cooperate in working towards a common goal and coordinate their activities in a harmonious fashion. As such, communication among individuals is necessarily a key component of distributed behavior, and we have identified three classes of distributed behavior that require communication: data-driven behaviors, where semantically meaningful data is transmitted between individuals; temporal behaviors, which are based on the relative timing of individuals' actions; and structural behaviors, which are responsible for maintaining the underlying communication network connecting individuals. Our results demonstrate that evolutionary algorithms can discover groups of individuals that exhibit each of these different classes of distributed behavior, and that these behaviors can be discovered both in isolation (e.g., evolving a purely data-driven algorithm) and in concert (e.g., evolving an algorithm that includes both data-driven and structural behaviors). As part of this research, we show that evolutionary algorithms can discover novel heuristics for distributed computing, and hint at a new class of distributed algorithm enabled by such studies.The majority of this research was conducted with the Avida platform for digital evolution, a system that has been proven to aid researchers in understanding the biological process of evolution by natural selection. For this reason, the results presented in this dissertation provide the foundation for future studies that examine how distributed behaviors evolved in nature. The close relationship between evolutionary biology and evolutionary algorithms thus aids our study of evolving algorithms for the next generation of distributed computing systems.
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