Here are the final results. The multi-island outperforms the standard GA while maintaining higher diversity. There are a lot of useful tips and tricks about GAs in this paper. GAs deserve more love.
@maximelabonne
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Multi-Island Genetic Algorithm for Diversity Maintenance
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Maintaining diversity is a big focus, so they implemented a "multi-island" GA. → Each population is divided into several sub-populations called "islands" → Applies GA separately on each sub-population, periodically migrating individuals between them
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Genetic Algorithm for Puzzle Solving with Diversity Selection
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Next, solving puzzles. The authors used a custom GA: 1/ Initialization using the previous algorithm 2/ Crossover/Mutation with a uniform crossover operation 3/ Find feasible solutions and "heal" broken ones 4/ Selection with a diversity criterion
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Graph Node Selection Algorithm With Constraint-Based Synergy Optimization
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Proposed solution: 1/ Defining a random traversal order for graph nodes 2/ Filtering the list of candidates according to constraint requirements 3/ Selecting an item that maximizes the synergy of partially filled neighboring candidates.
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Building High-Synergy Agent Formations and Optimal Puzzle Solutions
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The authors divide this problem into 2 subproblems: 1/ Building formations with high synergy 2/ Solve puzzles in a near-optimal way
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Optimizing Puzzle Generation: Beyond Exhaustive Search Methods
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Current landscape: • Traditional approaches to generating puzzles are often time-consuming • Exhaustive search may not be feasible due to the large design space
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Graph Genetic Algorithm Optimizes Video Game Puzzle Validation
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Automated Graph Genetic Algorithm based Puzzle Validation for Faster Game Design A funny evolutionary algorithm for solving puzzles in video games from @EA It doesn't only save time for designers but also helps to improve puzzle quality #AI https://
arxiv.org/abs/2302.09040 -
Social Distancing Rules in 24 Dimensions
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Hahaha imagine social distancing rules in 24 dimensions. So close yet so far…
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24-Dimensional Hypersphere Packing Problem in Computing
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When you stack hyperspheres in 24 dimensions, a sphere can touch 196,560 other hyperspheres and nobody likes it.
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Learnable Prompts Emerge as Key GNN Pre-training Architecture
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In conclusion, it looks like a neat architecture to pre-train GNNs. Learnable prompts are a handy addition and might re-appear in future architectures. Can't wait to become a graph prompt engineer.