20240054368. GEOGRAPHIC DISTRIBUTION OF RESOURCES USING GENETIC ALGORITHMS simplified abstract (WORKDAY, INC.)
Contents
GEOGRAPHIC DISTRIBUTION OF RESOURCES USING GENETIC ALGORITHMS
Organization Name
Inventor(s)
Volodymyr Tomenko of Pleasanton CA (US)
Dalmo Cirne of Boulder CO (US)
Ganesh Rajaratnam of Pleasanton CA (US)
Chris Chen of Pleasanton CA (US)
GEOGRAPHIC DISTRIBUTION OF RESOURCES USING GENETIC ALGORITHMS - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240054368 titled 'GEOGRAPHIC DISTRIBUTION OF RESOURCES USING GENETIC ALGORITHMS
Simplified Explanation
The patent application describes a method that involves:
- Initializing a population of hypotheses
- Computing misfit values for each hypothesis using a fitness function with weighted summation
- Generating offspring hypotheses based on the population and a crossover bitmask
- Creating a new population with the offspring and a subset of the original population
- Mutating at least one hypothesis in the new population
- Selecting a hypothesis from the new population based on its misfit value
- Allocating resources based on the selected hypothesis
Potential applications of this technology:
- Evolutionary algorithms
- Optimization problems in various fields such as engineering, finance, and biology
Problems solved by this technology:
- Finding optimal solutions to complex problems
- Handling large search spaces efficiently
Benefits of this technology:
- Improved efficiency in finding solutions
- Ability to handle complex and diverse problem spaces
- Adaptability to different problem domains.
Original Abstract Submitted
in some aspects, the techniques described herein relate to a method including: initializing a population of hypotheses; computing misfit values for each of the hypotheses, the misfit values computed using a fitness function including a weighted summation, wherein terms of weighted summation include metric functions; generating a plurality of offspring hypotheses based on the population of hypotheses and a crossover bitmask; generating a new population using the plurality of offspring and a subset of the population of hypotheses; mutating at least one hypothesis in the new population; selecting a hypothesis from the new population based on a corresponding misfit value of the hypothesis; and allocating at least one resource based on the hypothesis.
