TY - JOUR

T1 - Automatic generation of algorithms for robust optimisation problems using Grammar-Guided Genetic Programming

AU - Hughes, M.

AU - Goerigk, M.

AU - Dokka, T.

PY - 2021/9/30

Y1 - 2021/9/30

N2 - We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided Genetic Programming. We develop algorithmic building blocks in a Particle Swarm Optimisation framework, define the rules for constructing heuristics from these components, and evolve populations of search algorithms for robust problems. Our algorithmic building blocks combine elements of existing techniques and new features, resulting in the investigation of a novel heuristic solution space. We obtain algorithms which improve upon the current state of the art. We also analyse the component level breakdowns of the populations of algorithms developed against their performance, to identify high-performing heuristic components for robust problems. © 2021 The Authors

AB - We develop algorithms capable of tackling robust black-box optimisation problems, where the number of model runs is limited. When a desired solution cannot be implemented exactly the aim is to find a robust one, where the worst case in an uncertainty neighbourhood around a solution still performs well. To investigate improved methods we employ an automatic generation of algorithms approach: Grammar-Guided Genetic Programming. We develop algorithmic building blocks in a Particle Swarm Optimisation framework, define the rules for constructing heuristics from these components, and evolve populations of search algorithms for robust problems. Our algorithmic building blocks combine elements of existing techniques and new features, resulting in the investigation of a novel heuristic solution space. We obtain algorithms which improve upon the current state of the art. We also analyse the component level breakdowns of the populations of algorithms developed against their performance, to identify high-performing heuristic components for robust problems. © 2021 The Authors

KW - Genetic programming

KW - Global optimisation

KW - Implementation uncertainty

KW - Metaheuristics

KW - Robust optimisation

KW - Heuristic algorithms

KW - Particle swarm optimization (PSO)

KW - Automatic Generation

KW - Building blockes

KW - Component levels

KW - Grammar guided genetic programming

KW - Heuristic solutions

KW - Optimisation problems

KW - Search Algorithms

KW - Genetic algorithms

U2 - 10.1016/j.cor.2021.105364

DO - 10.1016/j.cor.2021.105364

M3 - Journal article

VL - 133

JO - Computers and Operations Research

JF - Computers and Operations Research

SN - 0305-0548

M1 - 105364

ER -