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MEER 111 – Global Research Solving Real-World Problems with Evolutionary Algorithms Daniel Tauritz, Ph. MEER 111 – Global Research Solving Real-World Problems with Evolutionary Algorithms Daniel Tauritz, Ph. D. Associate Professor of Computer Science

Algorithm An algorithm is a sequence of well-defined instructions that can be executed in Algorithm An algorithm is a sequence of well-defined instructions that can be executed in a finite amount of time in order to solve some problem.

Optimization Algorithm An optimization algorithm is an algorithm which takes as input a solution Optimization Algorithm An optimization algorithm is an algorithm which takes as input a solution space, an objective function which maps each point in the solution space to a linearly ordered set, and a desired goal element in the set.

Stochastic Algorithm A stochastic algorithm is an algorithm which when executed multiple times with Stochastic Algorithm A stochastic algorithm is an algorithm which when executed multiple times with the same input, produces different outputs drawn from some underlying probability distribution.

Evolutionary Algorithm A stochastic optimization algorithm inspired by genetics and natural evolution theory. Evolutionary Algorithm A stochastic optimization algorithm inspired by genetics and natural evolution theory.

Deriving Gas-Phase Exposure History through Computationally Evolved Inverse Diffusion Analysis • Joshua M. Eads Deriving Gas-Phase Exposure History through Computationally Evolved Inverse Diffusion Analysis • Joshua M. Eads • Former undergraduate student in Computer Science • Daniel Tauritz • Associate Professor of Computer Science • Glenn Morrison • Associate Professor of Environmental Engineering • Ekaterina Smorodkina • Former Ph. D. Student in Computer Science

Introduction Find Contaminants and Fix Issues Examine Indoor Exposure History Unexplained Sickness Introduction Find Contaminants and Fix Issues Examine Indoor Exposure History Unexplained Sickness

Background • Indoor air pollution top five environmental • • • health risks $160 Background • Indoor air pollution top five environmental • • • health risks $160 billion could be saved every year by improving indoor air quality Current exposure history is inadequate A reliable method is needed to determine past contamination levels and times

Problem Statement • A forward diffusion differential equation predicts concentration in materials after exposure Problem Statement • A forward diffusion differential equation predicts concentration in materials after exposure • An inverse diffusion equation finds the timing and intensity of previous gas contamination • Knowledge of early exposures would greatly strengthen epidemiological conclusions

Gas-phase concentration history and material absorption Gas-phase concentration history and material absorption

Proposed Solution • Use Genetic Programming (GP) as a directed search for inverse equation Proposed Solution • Use Genetic Programming (GP) as a directed search for inverse equation • Fitness based on x^5 + x^4 - tan(y) / pi x^2 + sin(x) sin(cos(x+y)^2) sin(x+y) + e^(x^2) 5 x^2 + 12 x - 4 x^2 - sin(x) X + Sin / forward equation ?

Related Research • It has been proven that the inverse • • equation exists Related Research • It has been proven that the inverse • • equation exists Symbolic regression with GP has successfully found both differential equations and inverse functions Similar inverse problems in thermodynamics and geothermal research have been solved

Interdisciplinary Work • Collaboration between Environmental Engineering, Computer Science, and Math Parent Selection Candidate Interdisciplinary Work • Collaboration between Environmental Engineering, Computer Science, and Math Parent Selection Candidate Solutions Competition Population Reproduction Fitness Genetic Programming Algorithm Forward Diffusion Equation

Genetic Programming Background + Y = X^2 + Sin( X * Pi ) Si Genetic Programming Background + Y = X^2 + Sin( X * Pi ) Si n * X X * X Pi

Summary • Ability to characterize exposure history will enhance ability to assess health risks Summary • Ability to characterize exposure history will enhance ability to assess health risks of chemical exposure

Coevolutionary Automated Software Correction (CASC) ISC Sponsored Project Ph. D. student: Josh Wilkerson Coevolutionary Automated Software Correction (CASC) ISC Sponsored Project Ph. D. student: Josh Wilkerson

Objective: Find a way to automate the process of software testing and correction. Approach: Objective: Find a way to automate the process of software testing and correction. Approach: Create Coevolutionary Automated Software Correction (CASC) system which will take a software artifact as input and produce a corrected version of the software artifact as output.

Coevolutionary Cycle Coevolutionary Cycle

Population Initialization Population Initialization

Population Initialization Population Initialization

Population Initialization Population Initialization

Population Initialization Population Initialization

Initial Evaluation Initial Evaluation

Initial Evaluation Initial Evaluation

Reproduction Phase Reproduction Phase

Reproduction Phase Reproduction Phase

Reproduction Phase Reproduction Phase

Evaluation Phase Evaluation Phase

Evaluation Phase Evaluation Phase

Competition Phase Competition Phase

Competition Phase Competition Phase

Termination Termination

Termination Termination