Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network
Abstract
:1. Introduction
2. 2 Satisfiability Logic Representation
- A set of m logical variables, . Each variable stores a binary value of that exemplify TRUE and FALSE, respectively.
- Each variable in can be set of literals, where positive literal and negative literal is defined as and , respectively.
- Consisting of a set of n distinct clauses, . Each is connected by logical AND (). Every k literals will form a single and connected by logical OR ().
3. Radial Basis Function Neural Network (RBFNN)
4. 2-Satisfiability Based Reverse Analysis Method (2SATRA) in RBFNN
4.1. Genetic Algorithm in RBFNN-2SATRA
4.2. Differential Evolution Algorithm in RBFNN-2SATRA
4.3. Particle Swarm Optimization Algorithm in RBFNN-2SATRA
4.4. Artificial Bee Colony Algorithm in RBFNN-2SATRA
4.5. Artificial Immune System Algorithm in RBFNN-2SATRA
5. Experimental Setup
6. Datasets Description
7. Results and Discussion
8. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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AIS | ABC | PSO | |||
Parameter | Value | Parameter | Value | Parameter | Value |
Number of iteration | 10,000 | No_Employed_bees | 50 | 0.6 | |
200 | No_Onlooker_bees | 50 | 2 | ||
Population size | 100 | No_Scout_bees | 1 | 2 | |
Limit | 1000 | = | [0,1] | ||
Trial | 10,000 | Number of iteration | 10,000 | ||
DE | GA | ||||
Parameter | Value | Parameter | Value | ||
Number of iteration | 10,000 | Number of iteration | 10,000 | ||
[0,1] | Selection type | Wheel selection | |||
[0,2] | Number of individuals | 50 | |||
Population | 50 | Mutation ratio | 1 | ||
Number of iteration | 10,000 | Mutation type | Uniform | ||
Crossover ratio | 1 |
Benchmark Datasets | Field | Attributes | Instances | Training Samples | Testing Samples |
---|---|---|---|---|---|
German Credit Dataset (GCR) | Finance | Duration of Credit (month) Payment Status of Previous Credit Amount Value Savings/Stocks Length of current employment Installment percent Creditability | 1000 | 600 | 400 |
Hepatitis Dataset (HR) | Medical | Sex Steroid Antiviral Fatigue Malaise Anorexia Die or live | 155 | 93 | 62 |
Congressional Voting Records Dataset (CVR) | Social Science | Handicapped infant Water-project-cost-sharing El-Salvador-aid Religious-groups-in-schools Aid-to-Nicaraguan-contras Immigration Rep/demo (P) | 435 | 261 | 174 |
Car Evaluation Dataset (CE) | Business | Buying Price Maintenance Doors Number Person Number Size Boot Safety Values | 1728 | 1037 | 691 |
Postoperative Patient Dataset (PP) | Medical | L-CORE (patient’s internal temperature in C) L-SURF (patient’s surface temperature in C) L-O2(oxygen saturation in %) L-BP (last measurement of blood pressure) CORE-STBL (stability of patient’s core temperature) BP-STBL (stability of patient’s blood pressure) Decision ADM-DECS(discharge decision) | 90 | 54 | 36 |
Data Set | Metric | Algorithms | ||||
---|---|---|---|---|---|---|
GA | DE | PSO | ABC | AIS | ||
German Credit Dataset | MAE | 0.2575 | 0.215 | 0.2125 | 0.1625 | 0.12 |
MAPE% | 0.064375 | 0.05375 | 0.053125 | 0.040625 | 0.03 | |
Hepatitis Dataset | MAE | 0.290323 | 0.193548 | 0.177419 | 0.145161 | 0.064516 |
MAPE% | 0.468262 | 0.312175 | 0.28616 | 0.234131 | 0.104058 | |
Congressional Voting Records Dataset | MAE | 0.298851 | 0.275862 | 0.224138 | 0.218391 | 0.183908 |
MAPE% | 0.171753 | 0.158541 | 0.128815 | 0.125512 | 0.104694 | |
Car Evaluation Dataset | MAE | 0.204052 | 0.193922 | 0.188133 | 0.086831 | 0.081042 |
MAPE% | 0.02953 | 0.028064 | 0.027226 | 0.012566 | 0.011728 | |
Postoperative Patient Dataset | MAE | 0.388889 | 0.361111 | 0.25 | 0.138889 | 0.111111 |
MAPE% | 1.080246 | 1.003086 | 0.694444 | 0.385802 | 0.308642 |
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Alzaeemi, S.A.; Sathasivam, S. Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network. Processes 2020, 8, 1295. https://doi.org/10.3390/pr8101295
Alzaeemi SA, Sathasivam S. Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network. Processes. 2020; 8(10):1295. https://doi.org/10.3390/pr8101295
Chicago/Turabian StyleAlzaeemi, Shehab Abdulhabib, and Saratha Sathasivam. 2020. "Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network" Processes 8, no. 10: 1295. https://doi.org/10.3390/pr8101295
APA StyleAlzaeemi, S. A., & Sathasivam, S. (2020). Artificial Immune System in Doing 2-Satisfiability Based Reverse Analysis Method via a Radial Basis Function Neural Network. Processes, 8(10), 1295. https://doi.org/10.3390/pr8101295