11 research outputs found
EMERGENCE OF COMPLEX STRATEGIES IN THE EVOLUTION OF CHESS ENDGAME PLAYERS
We examine a strong chess-endgame player, previously developed by us through genetic programming, focusing on the player's emergent capabilities and tactics in the context of a chess match. First, we provide a detailed description of the evolutionary approach by which our player was developed. Then, using a number of methods we analyze the evolved player's building blocks and their effect on play level. We conclude that evolution has found combinations of building blocks that are far from trivial and cannot be explained through simple combination — thereby indicating the possible emergence of complex strategies.Evolutionary algorithms, genetic programming, chess
Evolving Hyper Heuristic-Based Solvers for Rush Hour and FreeCell
We use genetic programming to evolve highly successful solvers for two puzzles: Rush Hour and FreeCell
Evolving Predictions for Executive Pay Features in Board Networks
Numerous recent studies in finance literature have shown that board networks are an important inter-corporate setting, influencing corporate decisions made by the board of directors, for example the determination of executive pay features. In this paper, we evolve predictors for the existence and adoption of several important pay features among S&P1500 companies, over the period 2006--2012. We use data from five well-known financial databases, including hundreds of variables containing both director-level and firm-level data. We present two approaches for predicting executive pay features. The first approach is based on a Genetic Algorithm (GA) used to evolve predictors based on weighted vectors of the predicting variables, providing relatively easy to understand prediction rules. The second approach employs Genetic Programming (GP) with sets of functions and terminals we devised specifically for this domain, based on contemporary research in finance. Thus, the GP approach explores a wider problem space and allows for more complex feature combinations. Experiments using both methods attain high quality prediction results, when compared to previous results in finance research. Additionally, our model is capable of successfully predicting combinations of pay features, compared to standard empirical models in finance, under various experimental conditions
Evolving Solvers for FreeCell and the Sliding-Tile Puzzle
We use genetic algorithms to evolve highly successful solvers for two puzzles: FreeCell and the Sliding-Tile Puzzle