A comparison on the performance of crossover techniques in video game

Soon, Gan Kim; Guan, Tan Tse; On, Chin Kim; Alfred, Rayner; Anthony, Patricia

doi:10.1109/iccsce.2013.6720015

Cited by 24 publications

(20 citation statements)

References 15 publications

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“…In this case, it was decided that the user would be given the freedom to choose the character they wanted rather than fixing a specific line of action that forced the user to follow a predefined sequence. To this end, a machine of finite states was utilized ( Soon et al, 2013 ) in which each state represented a situation in the game that the user had to experience. The sequence is the following (see Figure 2 ):…”

Section: Introductionmentioning

confidence: 99%

Stigma-Stop: A Serious Game against the Stigma toward Mental Health in Educational Settings

et al. 2017

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This paper presents the results from the application of a serious game called Stigma-Stop among a group of high school students with the aim of reducing the stigma toward mental illnesses. The video game features characters with various mental disorders (schizophrenia, depression, bipolar disorder, and panic disorder with agoraphobia) and provides information about these problems. Additionally, the game asks players about whether they have ever felt the same as the characters, if they believe the characters are psychologically well, and if they think they could help these individuals. Similarly, a variety of reactions are provided for players to choose from when they encounter the characters with these problems. A total of 552 students between the ages of 14 and 18 participated in the study, and they were randomly assigned to either the experimental group, which used Stigma-Stop, or the control group, which utilized a video game completely unrelated to mental health. Both video games were used for similar lengths of time. Following the application of Stigma-Stop, a statistically significant decrease was obtained in levels of stigma toward schizophrenia, both in terms of stereotypes and, to a greater extent, its potential dangerousness. However, this was not the case in the control group. Results thus demonstrate the video game’s usefulness toward eradicating erroneous notions about serious mental disorders like schizophrenia.

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Section: Introductionmentioning

confidence: 99%

Stigma-Stop: A Serious Game against the Stigma toward Mental Health in Educational Settings

et al. 2017

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“…• Next, the cross-over operation is initiated between the two selected parents to generate two new children, called T empCH1 and T empCH2. Here, we use the Single Point Cross-over technique [36] as an example. Specifically, entries of a randomly selected column of the two parents will be swapped to perform the single point cross-over.…”

Section: B Suboptimal Channel Allocationmentioning

confidence: 99%

Joint Beamforming, Power, and Channel Allocation in Multiuser and Multichannel Underlay MISO Cognitive Radio Networks

Dadallage

Cai

2016

IEEE Trans. Veh. Technol.

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In this paper, we consider a joint beamforming, power, and channel allocation in a multi-user and multi-channel underlay multiple input single output (MISO) cognitive radio network (CRN). In this system, primary users' (PUs') spectrum can be reused by the secondary user transmitters (SUTXs) to maximize the spectrum utilization while the intra-user interference is minimized by implementing beamforming at each SU-TX. After formulating the joint optimization problem as a non-convex, mixed integer nonlinear programming (MINLP) problem, we propose a solution which consists of two stages. In the first stage, a feasible solution for power allocation and beamforming vectors is derived under a given channel allocation by converting the original problem into a convex form with an introduced optimal auxiliary variable and semidefinite relaxation (SDR) approach. After that, in the second stage, two explicit searching algorithms, i.e., genetic algorithm (GA) and simulated annealing (SA)-based algorithm, are proposed to determine suboptimal channel allocations. Simulation results show that beamforming, power and channel allocation with SA (BPCA-SA) algorithm can achieve close-to-optimal sum-rate while having a lower computational complexity compared with beamforming, power and channel allocation with GA (BPCA-GA) algorithm. Furthermore, our proposed allocation scheme has significant improvement in achievable sum-rate compared to the existing zero-forcing beamforming (ZFBF).

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“…The algorithm does this by using a standard two-point crossover. 37 However, it differs from most literature because the crossover is performed without a following mutation. The mutation is such a small change compared to the crossover recombination.…”

Section: Evolutionary Algorithm Taskermentioning

confidence: 99%

Evolutionary sensor allocation for the Space Surveillance Network

Greve

Hopkinson

Lamont

2017

Journal of Defense Modeling & Simulation

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The congested exosphere continues to contain more satellites and debris, raising the potential for destructive collisions. The Special Perturbations (SP) Tasker algorithm currently assigns the ground sensors tasks to track object locations. Accurate locations help avoid collisions. However, the SP Tasker ignores priority, which is the satellite's importance factor. This article introduces the Evolutionary Algorithm Tasker (EAT) to solve the Satellite Sensor Allocation Problem (SSAP), which is a hybrid Evolutionary Strategy and Genetic Algorithm concept including specific techniques to explore the solution space and exploit the best solutions found. This approach goes beyond the current method, which does not include priority and other methods from the literature that have been applied to small-scale simulations. The SSAP model implementation extends Multi-Objective Evolutionary Algorithms (MOEAs) from the literature while accounting for priorities. Multiple real-world factors are considered, including each sensor's field-of-view, the orbital opportunities to track a satellite, the capacity of the sensor, and the relative priority of the satellites. The single objective EAT is statistically compared to the SP Tasker algorithm. Simulations show that both the EAT and MOEA approaches effectively use priority in the core tasking algorithms to ensure that higher priority satellites are tracked.

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A comparison on the performance of crossover techniques in video game

Cited by 24 publications

References 15 publications

Stigma-Stop: A Serious Game against the Stigma toward Mental Health in Educational Settings

Stigma-Stop: A Serious Game against the Stigma toward Mental Health in Educational Settings

Joint Beamforming, Power, and Channel Allocation in Multiuser and Multichannel Underlay MISO Cognitive Radio Networks

Evolutionary sensor allocation for the Space Surveillance Network

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