Our methods are published, not hidden. Every claim we make about ranking, matching and multi-objective optimization traces back to work you can read and cite.
Research
Research behind Genetic AI
From evolutionary data theory to feature weighting and ab initio multi-objective optimization — our latest work on how Genetic AI learns to rank, match and surprise.
arXiv preprint — cs.NE · 2026
Evolutionary Data Theory: On the Similarities between Data Problems and Evolutionary Games
P. Wissgott
Applying the concepts and formalism of Evolutionary Game Theory to the data regime, this paper introduces the fundamental paradigms of Evolutionary Data Theory. Replicator equations, evolutionary strategies, the Bishop-Cannings theorem and the analogy to Lotka-Volterra systems are mapped to the data interpretation, with input data understood as genes and organisms competing in an evolutionary game.
Feature weighting for data analysis via evolutionary simulation
A. Daniilidis, A. Domínguez Corella, P. Wissgott
An algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis. Weights — interpreted as feature relevance — evolve by a replicator-type dynamic on the standard simplex, and the resulting sequence is proven to converge globally to a unique interior equilibrium with non-degenerate limiting weights.
Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization
P. Wissgott
Genetic AI is a method for multi-objective optimization without external parameters, predefined weights or training data. Input data is converted into genes and organisms which compete for fitness in a simulation from first principles, governed by the Dominant, Altruistic, Balanced and Selfish evolutionary strategies.
While the world follows the hype of ever larger language models, this essay argues for a different direction: an AI built on the mechanics of evolution rather than the statistics of language. It contrasts the averaging, training-data-dependent nature of LLMs — with their bias, hallucinations and enormous energy footprint — against a lean, evolution-inspired approach that works from first principles.
Genetic AI: Applying evolutionary strategies to create a new type of artificial intelligence
P. Wissgott
The foundational whitepaper on Genetic AI. Inspired by genetic algorithms and evolutionary game theory, it develops a framework in which data features become genes and data records become organisms that interact under specific evolutionary strategies. Fast evolutionary simulations make real-time, decentralizable model training possible, with applications across data analysis and AI.
Evolutionary Data Theory: On the Similarities between Data Problems and Evolutionary Games
P. Wissgott
Applying the concepts and formalism of Evolutionary Game Theory to the data regime, this paper introduces the fundamental paradigms of Evolutionary Data Theory. Replicator equations, evolutionary strategies, the Bishop-Cannings theorem and the analogy to Lotka-Volterra systems are mapped to the data interpretation, with input data understood as genes and organisms competing in an evolutionary game.
Feature weighting for data analysis via evolutionary simulation
A. Daniilidis, A. Domínguez Corella, P. Wissgott
An algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis. Weights — interpreted as feature relevance — evolve by a replicator-type dynamic on the standard simplex, and the resulting sequence is proven to converge globally to a unique interior equilibrium with non-degenerate limiting weights.
Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization
P. Wissgott
Genetic AI is a method for multi-objective optimization without external parameters, predefined weights or training data. Input data is converted into genes and organisms which compete for fitness in a simulation from first principles, governed by the Dominant, Altruistic, Balanced and Selfish evolutionary strategies.
While the world follows the hype of ever larger language models, this essay argues for a different direction: an AI built on the mechanics of evolution rather than the statistics of language. It contrasts the averaging, training-data-dependent nature of LLMs — with their bias, hallucinations and enormous energy footprint — against a lean, evolution-inspired approach that works from first principles.
Genetic AI: Applying evolutionary strategies to create a new type of artificial intelligence
P. Wissgott
The foundational whitepaper on Genetic AI. Inspired by genetic algorithms and evolutionary game theory, it develops a framework in which data features become genes and data records become organisms that interact under specific evolutionary strategies. Fast evolutionary simulations make real-time, decentralizable model training possible, with applications across data analysis and AI.
Feature weighting for data analysis via evolutionary simulation
A. Daniilidis, A. Domínguez Corella, P. Wissgott
An algorithm for assigning weights prior to scalarization in discrete multi-objective problems arising from data analysis. Weights — interpreted as feature relevance — evolve by a replicator-type dynamic on the standard simplex, and the resulting sequence is proven to converge globally to a unique interior equilibrium with non-degenerate limiting weights.
Evolutionary Data Theory: On the Similarities between Data Problems and Evolutionary Games
P. Wissgott
Applying the concepts and formalism of Evolutionary Game Theory to the data regime, this paper introduces the fundamental paradigms of Evolutionary Data Theory. Replicator equations, evolutionary strategies, the Bishop-Cannings theorem and the analogy to Lotka-Volterra systems are mapped to the data interpretation, with input data understood as genes and organisms competing in an evolutionary game.
Genetic AI: Evolutionary Games for ab initio dynamic Multi-Objective Optimization
P. Wissgott
Genetic AI is a method for multi-objective optimization without external parameters, predefined weights or training data. Input data is converted into genes and organisms which compete for fitness in a simulation from first principles, governed by the Dominant, Altruistic, Balanced and Selfish evolutionary strategies.
While the world follows the hype of ever larger language models, this essay argues for a different direction: an AI built on the mechanics of evolution rather than the statistics of language. It contrasts the averaging, training-data-dependent nature of LLMs — with their bias, hallucinations and enormous energy footprint — against a lean, evolution-inspired approach that works from first principles.
Genetic AI: Applying evolutionary strategies to create a new type of artificial intelligence
P. Wissgott
The foundational whitepaper on Genetic AI. Inspired by genetic algorithms and evolutionary game theory, it develops a framework in which data features become genes and data records become organisms that interact under specific evolutionary strategies. Fast evolutionary simulations make real-time, decentralizable model training possible, with applications across data analysis and AI.