173,465 research outputs found
An evolutionary approach for interactive computer games
The authors would like to thank Jeong Keun Park for his
valuable contribution to the graphical representation of the
Dead End game.In this paper we introduce the first stage of experiments
on neuro-evolution mechanisms applied to predator/prey
multi-character computer games. Our test-bed is a computer
game where the prey (i.e. player) has to avoid its predators
by escaping through an exit without getting killed. By viewing
the game from the predatorsâ (i.e. opponentsâ) perspective, we
attempt off-line to evolve neural-controlled opponents capable of
playing effectively against computer-guided fixed strategy players.
Their efficiency is based on cooperation which emerges from
an abstract type of partial interaction with their environment. In
addition, investigation of behavior generalization demonstrated
the crucial contribution of playing strategies in the development
of successful predator behaviors.
However, emergent well-behaved opponents trained off-line
with fixed strategies do not make the game interesting to play. We
therefore present an evolutionary mechanism for opponents that
keep learning from a player while playing against it (i.e. on-line)
and we demonstrate its efficiency and robustness in increasing
the predatorsâ performance while altering their behavior as long
as the game is played. Computer game opponents following this
on-line learning approach show high adaptability to changing
player strategies, which provides evidence for the approachâs
effectiveness and interest against human players.peer-reviewe
Global search for occlusion minimisation in virtual camera control
This paper presents a fast and reliable global-search approach to the problem of virtual camera positioning when multiple objects that need to be within the reach of the camera are fully occluded. For this purpose, a comparative analysis of global-search algorithms is presented for the problem of maximising camera visibility across different tasks of varying complexity and within different real-time windows. A custom-designed genetic algorithm is compared to octree-based search and random search and results showcase the advantages of the genetic algorithm proposed with respect to efficiency, robustness and computational effort.peer-reviewe
Fusing novelty and surprise for evolving robot morphologies
Traditional evolutionary algorithms tend to converge to a single
good solution, which can limit their chance of discovering more
diverse and creative outcomes. Divergent search, on the other hand,
aims to counter convergence to local optima by avoiding selection
pressure towards the objective. Forms of divergent search such as
novelty or surprise search have proven to be beneficial for both
the efficiency and the variety of the solutions obtained in deceptive
tasks. Importantly for this paper, early results in maze navigation
have shown that combining novelty and surprise search yields an
even more effective search strategy due to their orthogonal nature.
Motivated by the largely unexplored potential of coupling novelty
and surprise as a search strategy, in this paper we investigate how
fusing the two can affect the evolution of soft robot morphologies.
We test the capacity of the combined search strategy against objective,
novelty, and surprise search, by comparing their efficiency and
robustness, and the variety of robots they evolve. Our key results
demonstrate that novelty-surprise search is generally more efficient
and robust across eight different resolutions. Further, surprise
search explores the space of robot morphologies more broadly than
any other algorithm examined.peer-reviewe
Shifting niches for community structure detection
We present a new evolutionary algorithm for community structure detection in both undirected and unweighted
(sparse) graphs and fully connected weighted digraphs (complete
networks). Previous investigations have found that, although
evolutionary computation can identify community structure in
complete networks, this approach seems to scale badly due to
solutions with the wrong number of communities dominating
the population. The new algorithm is based on a niching
model, where separate compartments of the population contain
candidate solutions with different numbers of communities. We
experimentally compare the new algorithm to the well-known
algorithms of Pizzuti and Tasgin, and find that we outperform
those algorithms for sparse graphs under some conditions, and
drastically outperform them on complete networks under all
tested conditions.peer-reviewe
RFreak-An R-package for evolutionary computation
RFreak is an R package providing a framework for evolutionary computation. By enwrapping the functionality of an evolutionary algorithm kit written in Java, it offers an easy way to do evolutionary computation in R. In addition, application examples where an evolutionary approach is promising in computational statistics are included and described in this paper. The package is thus further supporting the use of evolutionary computation in computational statistics. --R,evolutionary algorithms,evolutionary computation,association study,robust regression
Evolving card sets towards balancing dominion
In this paper we use the popular card game Dominion as a complex test-bed for the generation of interesting and balanced game rules. Dominion is a trading-card-like game where each card type represents a different game mechanic. Each playthrough only features ten different cards, the selection of which can form a new game each time. We compare and analyse three different agents that are capable of playing Dominion on different skill levels and use three different fitness functions to generate balanced card sets. Results reveal that there are particular cards of the game that lead to balanced games independently of player skill and behaviour. The approach taken could be used to balance other games with decomposable game mechanics.peer-reviewe
Integrating Evolutionary Computation with Neural Networks
There is a tremendous interest in the development of the evolutionary computation techniques as they are well suited to deal with optimization of functions containing a large number of variables. This paper presents a brief review of evolutionary computing techniques. It also discusses briefly the hybridization of evolutionary computation and neural networks and presents a solution of a classical problem using neural computing and evolutionary computing technique
A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing
With increasing demand for air travel and overloaded airport facilities, inefficient airport taxiing operations are a significant contributor to unnecessary fuel burn and a substantial source of pollution. Although taxiing is only a small part of a flight, aircraft engines are not optimised for taxiing speed and so contribute disproportionately to the overall fuel burn. Delays in taxiing also waste scarce airport resources and frustrate passengers. Consequently, reducing the time spent taxiing is an important investment. An exact algorithm for finding shortest paths based on A* allocates routes to aircraft that maintains aircraft at a safe distance apart, has been shown to yield efficient taxi routes. However, this approach depends on the order in which aircraft are chosen for allocating routes. Finding the right order in which to allocate routes to the aircraft is a combinatorial optimization problem in itself. We apply a rolling window approach incorporating a genetic algorithm for permutations to this problem, for real-world scenarios at three busy airports. This is compared to an exhaustive approach over small rolling windows, and the conventional first-come-firstserved ordering. We show that the GA is able to reduce overall taxi time with respect to the other approaches
Evolutionary Robot Vision for People Tracking Based on Local Clustering
This paper discusses the role of evolutionary computation in visual perception for partner robots. The search of evolutionary computation has many analogies with human visual search. First of all, we discuss the analogies between the evolutionary search and human visual search. Next, we propose the concept of evolutionary robot vision, and a human tracking method based on the evolutionary robot vision. Finally, we show experimental results of the human tracking to discuss the effectiveness of our proposed method
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