filmov
tv
Genetic programming is waiting for better tools - Rakhim Davletkaliyev
Показать описание
Genetic programming seems to be applicable in a very limited domain. Could the reason be the current state of software engineering?
What if we imagine the future of programming languages (or maybe the forgotten past), functional and homoiconic languages and better APIs (or lack thereof) in the next 20-40 years: could they enable genetic programming to finally thrive and work for common problems?
Computer scientists and engineers were thinking about universal modes of communication between computer programs since the 30s. The current state of API-driven communication is a nightmare by their standards, but we seem to only go deeper in it, solving more and more accidental complexity and losing energy and time.
LISPS continue to show us that good ideas come from simplicity, and the power of composition is the key to fighting complexity. I’d love to discuss how these ideas, if explored deeper, can allow genetic programming to evolve (pun intended) and solve real world problems at last.
What if we imagine the future of programming languages (or maybe the forgotten past), functional and homoiconic languages and better APIs (or lack thereof) in the next 20-40 years: could they enable genetic programming to finally thrive and work for common problems?
Computer scientists and engineers were thinking about universal modes of communication between computer programs since the 30s. The current state of API-driven communication is a nightmare by their standards, but we seem to only go deeper in it, solving more and more accidental complexity and losing energy and time.
LISPS continue to show us that good ideas come from simplicity, and the power of composition is the key to fighting complexity. I’d love to discuss how these ideas, if explored deeper, can allow genetic programming to evolve (pun intended) and solve real world problems at last.
0:37:45
Genetic programming is waiting for better tools - Rakhim Davletkaliyev
0:00:59
using Cartesian Genetic Programming
0:01:33
Genetic Algorithm Waiting for Sum stabilization
0:52:02
Genetic Programming – A New Approach - Aliyu Sambo
0:21:15
Genetic Programming in the Real World - Leonardo Trujillo and Daniel E. Hernández (ITT)
1:09:13
Deep Training: Tensorflow accelerated Genetic Programming
0:13:15
Robin Bakker, Ahmet Erdem : Genetic Regex | PyData Amsterdam 2019
0:15:22
AGI-15 Vita Batishcheva - Genetic Programming on Program Traces for Probabilistic Languages
0:10:39
Machine Learning Control: Genetic Programming Control
0:23:53
Using AI For Optimizing Strategies In Python
0:05:07
Genetic Programming
0:16:06
Generating Objected-Oriented Source Code Using Genetic Programming
0:32:58
Genetic programming in C using SDL — Part 1
0:05:34
WHAT'S A GENETIC ALGORITHM?
0:05:01
How Artificial intelligence learns | Genetic Algorithm explained
0:01:44
Genetic Algorithm defined in simple words | Darwin's theory of evolution in Artificial Intellig...
0:06:17
Genetic Programming Applications
0:13:54
GECCO2021 - wksp186 - WS - PDEIM - A Partially Asynchronous Global Parallel Genetic Algorithm
0:46:04
Genetic programming. Evolving expressions, programs and algorithms.
0:32:27
Joe Isaacson - Optimizing the Design of Genetic Programs for Living Cells
0:05:01
9.6: Genetic Algorithm: Improved Fitness Function - The Nature of Code
0:43:54
Evolution 3.0: Solve your everyday Problems with genetic Algorithms | Mey Beisaron
0:36:52
Optimization of Feature Learning through Grammar Guided Genetic Programming
0:11:34
Optimisation Lecture 3(a): Genetic Programming
Комментарии