43 research outputs found
Evolutionary Machine Learning and Games
Evolutionary machine learning (EML) has been applied to games in multiple
ways, and for multiple different purposes. Importantly, AI research in games is
not only about playing games; it is also about generating game content,
modeling players, and many other applications. Many of these applications pose
interesting problems for EML. We will structure this chapter on EML for games
based on whether evolution is used to augment machine learning (ML) or ML is
used to augment evolution. For completeness, we also briefly discuss the usage
of ML and evolution separately in games.Comment: 27 pages, 5 figures, part of Evolutionary Machine Learning Book
(https://link.springer.com/book/10.1007/978-981-99-3814-8
Generación automática de contenido para un nuevo juego basado en el problema de los tres cuerpos
Este trabajo presenta un algoritmo de generaci ón de contenido por procedimientos capaz de crear mapas completos para un videojuego que simula fenómenos fí sicos. El algoritmo evolutivo desarrollado intenta mejorar la difi cultad de los mapas. Además, gracias al uso de
poblaciones estructuradas, el algoritmo puede construir mapas que supongan un desafiío tanto a los jugadores más avanzados como a los m ás inexpertos.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Load Disaggregation Based on Aided Linear Integer Programming
Load disaggregation based on aided linear integer programming (ALIP) is proposed. We start with a conventional linear integer programming (IP) based disaggregation and enhance it in several ways. The enhancements include additional constraints, correction based on a state diagram, median filtering, and linear programming-based refinement. With the aid of these enhancements, the performance of IP-based disaggregation is significantly improved. The proposed ALIP system relies only on the instantaneous load samples instead of waveform signatures, and hence works well on low-frequency data. Experimental results show that the proposed ALIP system performs better than conventional IP-based load disaggregation
A Genetic Programming Strategy to Induce Logical Rules for Clinical Data Analysis
[EN]This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure
of the data and the rules found for each class, especially to track dichotomies and inequality.
The results reached by our proposal on the involved datasets have been very promising when used in classification tasks and compared with other methods
Forecasting Financial Volatility Using Nested Monte Carlo Expression Discovery
We are interested in discovering expressions for financial prediction using Nested Monte Carlo Search and Genetic Programming. Both methods are applied to learn from financial time series to generate non linear functions for market volatility prediction. The input data, that is a series of daily prices of European S&P500 index, is filtered and sampled in order to improve the training process. Using some assessment metrics, the best generated models given by both approaches for each training sub sample, are evaluated and compared. Results show that Nested Monte Carlo is able to generate better forecasting models than Genetic Programming for the majority of learning samples
A comparison of crossover control mechanisms within single-point selection hyper-heuristics using HyFlex
Hyper-heuristics are search methodologies which operate at a higher level of abstraction than traditional search and optimisation techniques. Rather than operating on a search space of solutions directly, a hyper-heuristic searches a space of low-level heuristics or heuristic components. An iterative selection hyper-heuristic operates on a single solution, selecting and applying a low-level heuristic at each step before deciding whether to accept the resulting solution. Crossover low-level heuristics are often included in modern selection hyper-heuristic frameworks, however as they require multiple solutions to operate, a strategy is required to manage potential solutions to use as input. In this paper we investigate the use of crossover control schemes within two existing selection hyper-heuristics and observe the difference in performance when the method for managing potential solutions for crossover is modified. Firstly, we use the crossover control scheme of AdapHH, the winner of an international competition in heuristic search, in a Modified Choice Function - All Moves selection hyper-heuristic. Secondly, we replace the crossover control scheme within AdapHH with another method taken from the literature. We observe that the performance of selection hyper-heuristics using crossover low level heuristics is not independent of the choice of strategy for managing input solutions to these operators
On the Impact of Operators and Populations within Evolutionary Algorithms for the Dynamic Weighted Traveling Salesperson Problem
Evolutionary algorithms have been shown to obtain good solutions for complex
optimization problems in static and dynamic environments. It is important to
understand the behaviour of evolutionary algorithms for complex optimization
problems that also involve dynamic and/or stochastic components in a systematic
way in order to further increase their applicability to real-world problems. We
investigate the node weighted traveling salesperson problem (W-TSP), which
provides an abstraction of a wide range of weighted TSP problems, in dynamic
settings. In the dynamic setting of the problem, items that have to be
collected as part of a TSP tour change over time. We first present a dynamic
setup for the dynamic W-TSP parameterized by different types of changes that
are applied to the set of items to be collected when traversing the tour. Our
first experimental investigations study the impact of such changes on resulting
optimized tours in order to provide structural insights of optimization
solutions. Afterwards, we investigate simple mutation-based evolutionary
algorithms and study the impact of the mutation operators and the use of
populations with dealing with the dynamic changes to the node weights of the
problem