- author = "Josefa Diaz-Alvarez and Pedro A Castillo and Francisco {Fernandez de Vega} and Francisco Chavez and Jorge Alvarado",
- title = "Population size influence on the energy consumption of genetic programming",
- journal = "Measurement and Control",
- year = "2022",
- volume = "55",
- number = "1-2",
- pages = "102--115",
- email = "fcofdez@unex.es",
- keywords = "genetic algorithms, genetic programming, energy consumption, evolutionary algorithms, energy-aware computing, performance measurements",
- ISSN = "0020-2940",
-
URL = "
https://journals.sagepub.com/doi/pdf/10.1177/00202940211064471",
-
DOI = "
doi:10.1177/00202940211064471",
- size = "14 pages",
-
abstract = "Evolutionary Algorithms (EAs) are routinely applied to
solve a large set of optimization problems.
Traditionally, their performance in solving those
problems is analyzed using the fitness quality and
computing time, and the effect of evolutionary
operators on both metrics is routinely used to compare
different versions of EAs. Nevertheless, scientists
face nowadays the challenge of considering the energy
efficiency in addition to computational time, which
requires studying the energy consumption of
algorithms.
This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested.Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution.
Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs.",
