DeepAI AI Chat
Log In Sign Up

Evolving Shepherding Behavior with Genetic Programming Algorithms

03/19/2016
by   Joshua Brulé, et al.
0

We apply genetic programming techniques to the `shepherding' problem, in which a group of one type of animal (sheep dogs) attempts to control the movements of a second group of animals (sheep) obeying flocking behavior. Our genetic programming algorithm evolves an expression tree that governs the movements of each dog. The operands of the tree are hand-selected features of the simulation environment that may allow the dogs to herd the sheep effectively. The algorithm uses tournament-style selection, crossover reproduction, and a point mutation. We find that the evolved solutions generalize well and outperform a (naive) human-designed algorithm.

READ FULL TEXT

page 1

page 2

page 3

page 4

03/16/2018

Towards Advanced Phenotypic Mutations in Cartesian Genetic Programming

Cartesian Genetic Programming is often used with a point mutation as the...
08/21/2021

Evolving Digital Circuits for the Knapsack Problem

Multi Expression Programming (MEP) is a Genetic Programming variant that...
10/22/2018

Scaling Up Cartesian Genetic Programming through Preferential Selection of Larger Solutions

We demonstrate how efficiency of Cartesian Genetic Programming method ca...
02/08/2021

Evolving Neuronal Plasticity Rules using Cartesian Genetic Programming

We formulate the search for phenomenological models of synaptic plastici...
06/25/2010

The Transfer of Evolved Artificial Immune System Behaviours between Small and Large Scale Robotic Platforms

This paper demonstrates that a set of behaviours evolved in simulation o...
11/06/2020

Learning Behavior Trees with Genetic Programming in Unpredictable Environments

Modern industrial applications require robots to be able to operate in u...
02/08/2021

Neurogenetic Programming Framework for Explainable Reinforcement Learning

Automatic programming, the task of generating computer programs complian...