#evolutionaryalgorithm — Public Fediverse posts

Moshe Sipper’s Cat-a-log of Writings
https://medium.com/@sipper/moshe-sippers-writings-174ed2e861df
#fiction #shortstories #stories #articles #artificialintelligence #deeplearning #machinelearning #evolutionaryalgorithm #geneticalgorithm

Evolutionary algorithm (Evolution 🧬)

In computational intelligence, an evolutionary algorithm is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play...

https://en.wikipedia.org/wiki/Evolutionary_algorithm

#EvolutionaryAlgorithm #Evolution #Cybernetics #EvolutionaryAlgorithms

I need to dig into the paper more deeply, but this seems like a pretty significant result for optimizing ML performance and power usage (at least for classifiers - but it seems like the same approach could be used elsewhere).

"Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline"

#ML #FPGA #evolutionaryalgorithm

https://arxiv.org/abs/2303.00031

I need to dig into the paper more deeply, but this seems like a pretty significant result for optimizing ML performance and power usage (at least for classifiers - but it seems like the same approach could be used elsewhere).

"Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline"

#ML #FPGA #evolutionaryalgorithm

https://arxiv.org/abs/2303.00031

I need to dig into the paper more deeply, but this seems like a pretty significant result for optimizing ML performance and power usage (at least for classifiers - but it seems like the same approach could be used elsewhere).

"Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline"

#ML #FPGA #evolutionaryalgorithm

https://arxiv.org/abs/2303.00031

I need to dig into the paper more deeply, but this seems like a pretty significant result for optimizing ML performance and power usage (at least for classifiers - but it seems like the same approach could be used elsewhere).

"Despite Tiny Classifiers being constrained to a few hundred logic gates, we observe no statistically significant difference in prediction performance in comparison to the best-performing ML baseline"

#ML #FPGA #evolutionaryalgorithm

https://arxiv.org/abs/2303.00031

Have you ever seen an #EvolutionaryAlgorithm evolve to "extinction"? By that I mean that fitness is gradually improving over multiple generations and then suddenly falls off a cliff to a lower bound, and never recovers. A student saw it in some code we were hacking recently. The cause was obvious, in retrospect.

Have you ever seen an #EvolutionaryAlgorithm evolve to "extinction"? By that I mean that fitness is gradually improving over multiple generations and then suddenly falls off a cliff to a lower bound, and never recovers. A student saw it in some code we were hacking recently. The cause was obvious, in retrospect.

Have you ever seen an #EvolutionaryAlgorithm evolve to "extinction"? By that I mean that fitness is gradually improving over multiple generations and then suddenly falls off a cliff to a lower bound, and never recovers. A student saw it in some code we were hacking recently. The cause was obvious, in retrospect.

Have you ever seen an #EvolutionaryAlgorithm evolve to "extinction"? By that I mean that fitness is gradually improving over multiple generations and then suddenly falls off a cliff to a lower bound, and never recovers. A student saw it in some code we were hacking recently. The cause was obvious, in retrospect.

Have you ever seen an #EvolutionaryAlgorithm evolve to "extinction"? By that I mean that fitness is gradually improving over multiple generations and then suddenly falls off a cliff to a lower bound, and never recovers. A student saw it in some code we were hacking recently. The cause was obvious, in retrospect.

MOSA-EA introduced by Xiaoyu Qin and myself is an #evolutionaryalgorithm for hard combinatorial optimisation problems. It builds on a decade of theoretical research on non-elitist, self-adaptive, population-based evolutionary algorithms.

It has excellent performance, particularly on random #MAXSAT and #NKlandscape problem instances.

Python and C/C++ source code available here:

https://github.com/ChengCheng-Qin/mosa-ea

MOSA-EA introduced by Xiaoyu Qin and myself is an #evolutionaryalgorithm for hard combinatorial optimisation problems. It builds on a decade of theoretical research on non-elitist, self-adaptive, population-based evolutionary algorithms.

It has excellent performance, particularly on random #MAXSAT and #NKlandscape problem instances.

Python and C/C++ source code available here:

https://github.com/ChengCheng-Qin/mosa-ea

MOSA-EA introduced by Xiaoyu Qin and myself is an #evolutionaryalgorithm for hard combinatorial optimisation problems. It builds on a decade of theoretical research on non-elitist, self-adaptive, population-based evolutionary algorithms.

It has excellent performance, particularly on random #MAXSAT and #NKlandscape problem instances.

Python and C/C++ source code available here:

https://github.com/ChengCheng-Qin/mosa-ea

MOSA-EA introduced by Xiaoyu Qin and myself is an #evolutionaryalgorithm for hard combinatorial optimisation problems. It builds on a decade of theoretical research on non-elitist, self-adaptive, population-based evolutionary algorithms.

It has excellent performance, particularly on random #MAXSAT and #NKlandscape problem instances.

Python and C/C++ source code available here:

https://github.com/ChengCheng-Qin/mosa-ea

A #thread of recent papers coauthored by Eörs Szathmáry on learning and evolution, covering Darwinian principles in the immune system, brain, Bayesian learning theory.

Mentioned in today's #KLIAustria #KLIColloquium talk.

#evolution #ecoevo #EvolutionaryBiology #EvolutionaryAlgorithm #Darwin #Darwinian #Learning #LearningTheory #Bayesian #neuroscience #immunology #PaperThread #

A #thread of recent papers coauthored by Eörs Szathmáry on learning and evolution, covering Darwinian principles in the immune system, brain, Bayesian learning theory.

Mentioned in today's #KLIAustria #KLIColloquium talk.

#evolution #ecoevo #EvolutionaryBiology #EvolutionaryAlgorithm #Darwin #Darwinian #Learning #LearningTheory #Bayesian #neuroscience #immunology #PaperThread #

A #thread of recent papers coauthored by Eörs Szathmáry on learning and evolution, covering Darwinian principles in the immune system, brain, Bayesian learning theory.

Mentioned in today's #KLIAustria #KLIColloquium talk.

#evolution #ecoevo #EvolutionaryBiology #EvolutionaryAlgorithm #Darwin #Darwinian #Learning #LearningTheory #Bayesian #neuroscience #immunology #PaperThread #

A #thread of recent papers coauthored by Eörs Szathmáry on learning and evolution, covering Darwinian principles in the immune system, brain, Bayesian learning theory.

Mentioned in today's #KLIAustria #KLIColloquium talk.

#evolution #ecoevo #EvolutionaryBiology #EvolutionaryAlgorithm #Darwin #Darwinian #Learning #LearningTheory #Bayesian #neuroscience #immunology #PaperThread #

A #thread of recent papers coauthored by Eörs Szathmáry on learning and evolution, covering Darwinian principles in the immune system, brain, Bayesian learning theory.

Mentioned in today's #KLIAustria #KLIColloquium talk.

#evolution #ecoevo #EvolutionaryBiology #EvolutionaryAlgorithm #Darwin #Darwinian #Learning #LearningTheory #Bayesian #neuroscience #immunology #PaperThread #