iCog-Labs-Dev/metta-moses

AI Summary: Refactor the `getBetterCandidates` function in the MOSES (Meta-Optimizing Semantic Evolutionary Search) MeTTa implementation to leverage the ordered set's inherent properties (uniqueness and sorted order) for improved performance and code quality. This involves modifying the function's logic to utilize these properties instead of treating the ordered set as a generic list.

AI Summary: Optimize the MeTTa implementation of the MOSES algorithm's `transform` function to improve runtime performance by eliminating redundant score removal before rescoring. This involves modifying the function to directly utilize the `first` or `second` functions during list mapping to extract the necessary element, thereby avoiding unnecessary recursions.

AI Summary: Refactor the MeTTa function `merger` from the `cross-top-one.metta` file to use built-in MeTTa functions like `map-atom` and `fold-atom` instead of non-deterministic methods. This will improve code reusability and maintain performance.