Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization
Fuente:
arXiv
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912365605289984 |
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| author | Wissgott, Philipp |
| author_facet | Wissgott, Philipp |
| contents | We introduce Genetic AI, a novel method for multi-objective optimization without external parameters or predefined weights. The method can be applied to all problems that can be formulated in matrix form and allows for a data-less training of AI models. Without employing predefined rules or training data, Genetic AI first converts the input data into genes and organisms. In a simulation from first principles, these genes and organisms compete for fitness, where their behavior is governed by universal evolutionary strategies. We present four evolutionary strategies: Dominant, Altruistic, Balanced and Selfish and show how a linear combination can be employed in a fully self-consistent evolutionary game. Investigating fitness and evolutionary stable equilibriums, Genetic AI helps solving optimization problems with a set of predefined, discrete solutions that change dynamically. We show the universality of the approach on two decision problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_19113 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization Wissgott, Philipp Neural and Evolutionary Computing 62, 65, 91, 49 F.2.2; G.1.6; G.4; H.1.m; I.2.8; I.2.m; I.6.5 We introduce Genetic AI, a novel method for multi-objective optimization without external parameters or predefined weights. The method can be applied to all problems that can be formulated in matrix form and allows for a data-less training of AI models. Without employing predefined rules or training data, Genetic AI first converts the input data into genes and organisms. In a simulation from first principles, these genes and organisms compete for fitness, where their behavior is governed by universal evolutionary strategies. We present four evolutionary strategies: Dominant, Altruistic, Balanced and Selfish and show how a linear combination can be employed in a fully self-consistent evolutionary game. Investigating fitness and evolutionary stable equilibriums, Genetic AI helps solving optimization problems with a set of predefined, discrete solutions that change dynamically. We show the universality of the approach on two decision problems. |
| title | Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization |
| topic | Neural and Evolutionary Computing 62, 65, 91, 49 F.2.2; G.1.6; G.4; H.1.m; I.2.8; I.2.m; I.6.5 |
| url | https://arxiv.org/abs/2501.19113 |