Variational Autoencoder Based Estimation Of Distribution Algorithms And Applications To Individual Based Ecosystem Modeling Using EcoSim

Individual based modeling provides a bottom up approach wherein interactions give rise to high-level phenomena in patterns equivalent to those found in nature. This method generates an immense amount of data through artificial simulation and can be made tractable by machine learning where multidimensional data is optimized and transformed. Using individual based modeling platform known as EcoSim, we modeled the abilities of elitist sexual selection and communication of fear. Data received from these experiments was reduced in dimension through use of a novel algorithm proposed by us: Variational Autoencoder based Estimation of Distribution Algorithms with Population Queue and Adaptive Variance Scaling (VAE-EDA-Q AVS). We constructed a novel Estimation of Distribution Algorithm (EDA) by extending generative models known as variational autoencoders (VAE). VAE-EDA-Q, proposed by us, smooths the data generation process using an iteratively updated queue (Q) of populations. Adaptive Variance Scaling (AVS) dynamically updates the variance at which models are sampled based on fitness. The combination of VAE-EDA-Q with AVS demonstrates high computational efficiency and requires few fitness evaluations. We extended VAE-EDA-Q AVS to act as a feature reducing wrapper method in conjunction with C4.5 Decision trees to reduce the dimensionality of data. The relationship between sexual selection, random selection, and speciation is a contested topic. Supporting evidence suggests sexual selection to drive speciation. Opposing evidence contends either a negative or absence of correlation to exist. We utilized EcoSim to model elitist and random mate selection. Our results demonstrated a significantly lower speciation rate, a significantly lower extinction rate, and a significantly higher turnover rate for sexual selection groups. Species diversification was found to display no significant difference. The relationship between communication and foraging behavior similarly features opposing hypotheses in claim of both increases and decreases of foraging behavior in response to alarm communication. Through modeling with EcoSim, we found alarm communication to decrease foraging activity in most cases, yet gradually increase foraging activity in some other cases. Furthermore, we found both outcomes resulting from alarm communication to increase fitness as compared to non-communication

Pathfinding by demand sensitive map abstraction

In this thesis, we present a new algorithm: Demand Sensitive Map Abstraction (DSMA). DSMA is a special kind of hierarchical pathfinding algorithm in which we vary the granularity of abstraction of the high-level map based on pathfinding request demand associated with various regions in the high level map and the search time of the last path request. Additionally, the low level A* search is not restricted by the boundaries of the high level sectors. By dynamically varying the abstraction we are able to maintain a balance between path quality and search time. We compare DSMA with two variations where the granularity of abstraction is constant; one of those contains maximum granularity throughout (Dense HA*) and the other contains the minimum (Sparse HA*). Our experimental results show that DSMA\u27s performance is a balance between Dense HA* and Sparse HA*. Depending on the resources available DSMA can behave either as Dense HA* or as Sparse HA* or lie somewhere in between. Moreover we do not pre-cache paths at any level, which gives us the added benefit of working with a flexible abstract map without the necessity of changing the pre-cached paths if the low level map changes