Towards Evolutionary Control Laws for Viability Problems
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{tonda:2023:GECCO,
-
author = "Alberto Tonda and Isabelle Alvarez and
Sophie Martin and Giovanni Squillero and Evelyne Lutton",
-
title = "Towards Evolutionary Control Laws for Viability
Problems",
-
booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
-
year = "2023",
-
editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
-
pages = "1464--1472",
-
address = "Lisbon, Portugal",
-
series = "GECCO '23",
-
month = "15-19 " # jul,
-
organisation = "SIGEVO",
-
publisher = "Association for Computing Machinery",
-
publisher_address = "New York, NY, USA",
-
keywords = "genetic algorithms, genetic programming, viable
feedback, machine learning, viability theory",
-
isbn13 = "9798400701191",
-
DOI = "doi:10.1145/3583131.3590415",
-
size = "9 pages",
-
abstract = "The mathematical theory of viability, developed to
formalize problems related to natural and social
phenomena, investigates the evolution of dynamical
systems under constraints. A main objective of this
theory is to design control laws to keep systems inside
viable domains. Control laws are traditionally defined
as rules, based on the current position in the state
space with respect to the boundaries of the viability
kernel. However, finding these boundaries is a
computationally expensive procedure, feasible only for
trivial systems. We propose an approach based on
Genetic Programming (GP) to discover control laws for
viability problems in analytic form. Such laws could
keep a system viable without the need of computing its
viability kernel, facilitate communication with
stakeholders, and improve explainability. A candidate
set of control rules is encoded as GP trees describing
equations. Evaluation is noisy, due to stochastic
sampling: initial conditions are randomly drawn from
the state space of the problem, and for each, a system
of differential equations describing the system is
solved, creating a trajectory. Candidate control laws
are rewarded for keeping viable as many trajectories as
possible, for as long as possible. The proposed
approach is evaluated on established benchmarks for
viability and delivers promising results.",
-
notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Alberto Tonda
Isabelle Alvarez
Sophie Martin
Giovanni Squillero
Evelyne Lutton
Citations