Evolutionary Contextual Bandits Algorithm Optimization
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Solution Overview
Problem
Current bandit algorithms face challenges in achieving high applicational accuracy and effectivity in solving contextual bandit problems.
Innovation Solution
The Evolution Linear Thompson Sampling (ELINTS) algorithm is introduced, which involves initializing a population of genomes for exploration parameters and feature subsets, calculating expected rewards, selecting action arms, observing rewards, and updating exploration parameters and genomes using genetic algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional bandit algorithms are used, then the solution process is simple, but the applicational accuracy and effectivity are insufficient
Solution Approach 1:
The patent combines evolutionary computation with contextual bandit algorithms to create a hybrid approach. The evolutionary algorithm maintains a population of candidate solutions (genomes) that encode different arm-selection policies, while the contextual bandit component evaluates these policies using Thompson Sampling. This merging allows the system to achieve higher applicational accuracy by exploring a broader solution space through population-based optimization while maintaining the theoretical guarantees of bandit algorithms.
Solution Approach 2:
The algorithm segments the solution representation into genomes that encode multiple parameters including exploration parameters (beta values for Thompson Sampling) and feature subset selections. Each genome represents a complete arm-selection policy that can be independently evaluated and optimized. This segmentation allows the evolutionary algorithm to manipulate and optimize different aspects of the policy separately through mutation and crossover operations.
2Adaptability or versatility
If exploration is increased to find better solutions, then the solution space coverage improves, but the cumulative reward decreases in the short term
Solution Approach 1:
The algorithm dynamically adjusts the exploration-exploitation balance through the evolutionary process. Early in the optimization process, genomes with higher exploration parameters (larger beta values) are favored, allowing broad solution space coverage. As the evolutionary algorithm progresses and identifies promising regions of the solution space, the population naturally shifts toward genomes with more exploitative behaviors that maximize cumulative reward. This dynamic adaptation resolves the contradiction by allowing both extensive exploration and high reward accumulation at different stages of the optimization process.
Solution Approach 2:
The evolutionary algorithm optimizes the exploration parameters (beta values) of the Thompson Sampling algorithm as part of the genome encoding. By treating these parameters as evolutionary variables subject to mutation and selection, the system automatically discovers the optimal level of exploration for different problem instances and stages of learning, rather than using fixed exploration rates. This parameter optimization enables the system to achieve both broad solution space coverage and high cumulative reward.
Data Source
AI summary
A method for solving a contextual bandit problem using an Evolution Linear Thompson Sampling (ELINTS) algorithm is provided, wherein the method includes identifying a contextual bandit problem having exploration parameters and feature subsets, initializing a population of genomes for use with the exploration parameters and the feature subset, initializing exploration parameter values and a random feature subset, calculating an expected reward using the exploration parameters and the feature subsets, choosing an action arm A(t), observing a reward R(t) and update a cumulative reward, selecting a subset of existing genomes based on the cumulative and replacing one or more of the existing genomes with newly created offspring genomes.