Use of a genetic algorithm to find solutions to introductory physics problems
In this work, we show how a genetic algorithm (GA) can be used to find step-by-step solutions to introductory physics problems. Our perspective is that the underlying task for this is one of finding a sequence of equations that will lead to the needed answer. Here a GA is used to find an appropriate equation sequence by minimizing a fitness function that measures the difference between the number of unknowns versus knowns in a set of equations. Information about knowns comes from the GA posing questions to the student about what quantities exist in the text of their problem. The questions are generated from enumerations pulled from the chromosomes that drive the GA. Equations with smaller known vs. unknown differences are considered more fit and are used to produce intermediate results that feed less fit equations. We show that this technique can guide a student to an answer to any introductory physics problem involving one-dimensional kinematics. Interpretability findings are discussed.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A Novel Genetic Algorithm using Helper Objectives for the 0-1 Knapsack Problem
The 0-1 knapsack problem is a well-known combinatorial optimisation problem. Approximation algorithms have been designed for solving it and they return provably good solutions within polynomial time. On the other hand, g…
Introductory review to quantum information retrieval
Recently people started to understand that applications of the mathematical formalism of quantum theory are not reduced to physics. Nowadays, this formalism is widely used outside of quantum physics, in particular, in co…
Decision MakingInformation RetrievalRetrievalGCLIQUE: An Open Source Genetic Algorithm for the Maximum Clique Problem
A clique in a graph is a set of vertices that are all connected to each other. A maximum clique is a clique of maximum size. A graph may have more than one maximum cliques. The problem of finding a maximum clique is a st…
C++ codeLevel-Based Analysis of Genetic Algorithms for Combinatorial Optimization
The paper is devoted to upper bounds on run-time of Non-Elitist Genetic Algorithms until some target subset of solutions is visited for the first time. In particular, we consider the sets of optimal solutions and the set…
Combinatorial OptimizationSolving the Schrodinger equation with genetic algorithms: a practical approach
The Schrodinger equation is one of the most important equations in physics and chemistry and can be solved in the simplest cases by computer numerical methods. Since the beginning of the 70s of the last century the compu…
Stochastic Optimization