{'fraction_non_alphanumeric': 0.01986301369863014, 'fraction_numerical': 0.0, 'mean_word_length': 5.436123348017621, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Hybrid genetic approach for the dynamic weapon-target allocation problem

This paper addresses the problem of threat engagement and dynamic weapon-target allocation (WTA) across the force or network-centric force optimization. The objective is to allocate and schedule defensive weapon resources over a given period of time so as to minimize surviving target value subject to resource availability and temporal constraints. The dynamic WTA problem is a NP-complete problem and belongs to a class of multiple-resource-constrained optimal scheduling problems. Inherent complexities in the problem of determining the optimal solution include limited weapon resources, time windows under which threats must be engaged, load-balancing across weapon systems, and complex interdependencies of various assignments and resources. We present a new hybrid genetic algorithm (GA) which is a combination of a traditional genetic algorithm and a simulated annealing-type algorithm for solving the dynamic WTA problem. The hybrid GA approach proposed here uses a simulated annealing-type heuristics to compute the fitness of a GA-selected population. This step also optimizes the temporal dimension (scheduling) under resource and temporal constraints. The proposed method provides schedules that are near-optimal in short cycle times and have minimal perturbation from one cycle to the next. We compare the performance of the proposed approach with a baseline WTA algorithm.