Deep Neural Crossover
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916329920921600 |
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| author | Shem-Tov, Eliad Elyasaf, Achiya |
| author_facet | Shem-Tov, Eliad Elyasaf, Achiya |
| contents | We present a novel multi-parent crossover operator in genetic algorithms (GAs) called ``Deep Neural Crossover'' (DNC). Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of deep reinforcement learning (DRL) and an encoder-decoder architecture to select the genes. Specifically, we use DRL to learn a policy for selecting promising genes. The policy is stochastic, to maintain the stochastic nature of GAs, representing a distribution for selecting genes with a higher probability of improving fitness. Our architecture features a recurrent neural network (RNN) to encode the parental genomes into latent memory states, and a decoder RNN that utilizes an attention-based pointing mechanism to generate a distribution over the next selected gene in the offspring. To improve the training time, we present a pre-training approach, wherein the architecture is initially trained on a single problem within a specific domain and then applied to solving other problems of the same domain. We compare DNC to known operators from the literature over two benchmark domains -- bin packing and graph coloring. We compare with both two- and three-parent crossover, outperforming all baselines. DNC is domain-independent and can be easily applied to other problem domains. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11159 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Neural Crossover Shem-Tov, Eliad Elyasaf, Achiya Neural and Evolutionary Computing We present a novel multi-parent crossover operator in genetic algorithms (GAs) called ``Deep Neural Crossover'' (DNC). Unlike conventional GA crossover operators that rely on a random selection of parental genes, DNC leverages the capabilities of deep reinforcement learning (DRL) and an encoder-decoder architecture to select the genes. Specifically, we use DRL to learn a policy for selecting promising genes. The policy is stochastic, to maintain the stochastic nature of GAs, representing a distribution for selecting genes with a higher probability of improving fitness. Our architecture features a recurrent neural network (RNN) to encode the parental genomes into latent memory states, and a decoder RNN that utilizes an attention-based pointing mechanism to generate a distribution over the next selected gene in the offspring. To improve the training time, we present a pre-training approach, wherein the architecture is initially trained on a single problem within a specific domain and then applied to solving other problems of the same domain. We compare DNC to known operators from the literature over two benchmark domains -- bin packing and graph coloring. We compare with both two- and three-parent crossover, outperforming all baselines. DNC is domain-independent and can be easily applied to other problem domains. |
| title | Deep Neural Crossover |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2403.11159 |