Optimizing Sequential Arrangements via Statistical Sequence Evaluation
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Solution Overview
Problem
Current methods lack a general-purpose solution for optimizing sequential arrangements across various industries, particularly for NP-Complete problems which are computationally challenging due to their exponential time complexity, limiting their applicability to large-scale sequencing issues.
Innovation Solution
A system and method utilizing a computer processor with encoded instructions to manage an initiating unit, tracking unit, generating unit, measuring unit, and regulating unit to optimize sequential arrangements by statistically treating and evaluating sequences based on a predetermined algorithm, ensuring efficient storage and generation of best sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sequencing methods are used to solve NP-Complete problems, then solution accuracy may be maintained for small problems, but computation time grows exponentially with problem size
Solution Approach 1:
The patent segments the sequencing problem into multiple components: an initiating unit that generates initial sequences, a tracking unit that maintains and evaluates sequences, a generating unit that creates new sequences, a measuring unit that evaluates sequence quality, and a regulating unit that controls the optimization process. This segmentation allows the system to handle large-scale NP-Complete problems by processing them in manageable stages rather than attempting exhaustive search.
Solution Approach 2:
The initiating unit performs preliminary action by generating a predetermined number of initial sequences before the main optimization process begins. The tracking unit pre-processes these sequences by storing them and effecting statistical treatment, preparing the foundation for subsequent optimization iterations. This preliminary preparation reduces the computational burden during the main optimization phase.
2Reliability
If exhaustive search methods are used to ensure optimal sequencing, then solution quality is maximized, but computational complexity becomes intractable for large problems
Solution Approach 1:
The measuring unit continuously evaluates new sequences against the predetermined evaluating algorithm and provides feedback to the tracking unit. The tracking unit uses this feedback to determine whether to store and statistically treat the new sequence. The regulating unit receives efficacy indications and uses this feedback to control whether the generating unit should create more sequences or output the best sequence found. This feedback mechanism ensures solution quality without requiring exhaustive search.
Solution Approach 2:
The system changes parameters dynamically during the optimization process. The regulating unit responds to predetermined criteria to adjust the generation process, and the statistical treatment of sequences evolves as better sequences are found. This parameter adaptation allows the system to maintain solution quality while reducing computational complexity compared to static exhaustive search methods.
3Measurement precision
If statistical treatment is applied to all sequences, then comprehensive evaluation is achieved, but storage and processing requirements increase significantly
Solution Approach 1:
The tracking unit applies statistical treatment selectively rather than to all possible sequences. It stores and effects statistical treatment on initial sequences and on new sequences that meet specific criteria (being different and better than previously stored sequences). This partial application of statistical treatment achieves comprehensive evaluation of relevant sequences while avoiding the prohibitive storage requirements of evaluating all possible sequences.
Data Source
AI summary
A tracking unit includes a register for storing initial sequences and an accumulator for effecting a statistical treatment of the initial sequences, and ensures the statistical treatment relates to no more than a number of best sequences based on an evaluating algorithm. A generating unit employs the statistical treatment to present a new sequence. A measuring unit evaluates the new sequence according to the algorithm and cooperates with the tracking unit to effect providing the new sequence to the tracking unit when the evaluating indicates the new sequence is appropriate for the storing and statistical treatment. The measuring unit provides the new sequence to a regulating unit with an indication of efficacy of the new sequence. The regulating unit employs the indication to store a best-sequence-yet-received and responds to a criterion to order the generating unit to present a new sequence or to present the best-sequence-yet-received at an output locus.