Attempt: Globalscape

Admins can monitor these attempts in the cl[yymmdd].log files. A "GetLastCode" of 0 indicates a successful attempt, while codes like 5 (connection refused) or 11 (Socks5 authentication failure) point to specific issues. 3. Security: Blocking Malicious Access Attempts

🔁 RT to warn your SOC team.

PROGRAM GLOBALSCAPE C C A PROGRAM TO SIMULATE A GLOBAL OPTIMIZATION ATTEMPT C USING A SIMPLIFIED GENETIC ALGORITHM ON A LANDSCAPE FUNCTION. C INTEGER NPOP, NGEN, NVAR PARAMETER (NPOP = 20, NGEN = 50, NVAR = 2) C DOUBLE PRECISION POP(NPOP, NVAR), FITNESS(NPOP) DOUBLE PRECISION BEST_SOLUTION(NVAR), BEST_FITNESS DOUBLE PRECISION LOWER_BOUND, UPPER_BOUND INTEGER I, J, G C C INITIALIZE PARAMETERS LOWER_BOUND = -5.0D0 UPPER_BOUND = 5.0D0 BEST_FITNESS = 1.0D20 C C INITIALIZE RANDOM POPULATION CALL INIT_POP(POP, NPOP, NVAR, LOWER_BOUND, UPPER_BOUND) C C MAIN GENERATION LOOP DO 100 G = 1, NGEN C C EVALUATE FITNESS FOR EACH INDIVIDUAL DO 50 I = 1, NPOP FITNESS(I) = OBJECTIVE_FUNC(POP(I, 1), POP(I, 2)) 50 CONTINUE C C UPDATE BEST SOLUTION FOUND SO FAR DO 60 I = 1, NPOP IF (FITNESS(I) .LT. BEST_FITNESS) THEN BEST_FITNESS = FITNESS(I) DO 40 J = 1, NVAR BEST_SOLUTION(J) = POP(I, J) 40 CONTINUE END IF 60 CONTINUE C C CREATE NEXT GENERATION (SELECTION + CROSSOVER + MUTATION) CALL NEXT_GENERATION(POP, FITNESS, NPOP, NVAR, & LOWER_BOUND, UPPER_BOUND) C 100 CONTINUE C C OUTPUT RESULTS WRITE(*,*) 'OPTIMIZATION COMPLETE.' WRITE(*,*) 'BEST FITNESS FOUND:', BEST_FITNESS WRITE(*,*) 'BEST SOLUTION (X, Y):', (BEST_SOLUTION(J), J=1,NVAR) C END C C------------------------------------------------------------ C DOUBLE PRECISION FUNCTION OBJECTIVE_FUNC(X, Y) DOUBLE PRECISION X, Y C RASTRIGIN FUNCTION (Simplified) OBJECTIVE_FUNC = (X**2 - 10.0D0*DCOS(2.0D0*3.14159D0*X)) + & (Y**2 - 10.0D0*DCOS(2.0D0*3.14159D0*Y)) + 20.0D0 RETURN END C C------------------------------------------------------------ C SUBROUTINE INIT_POP(POP, N, D, MIN_VAL, MAX_VAL) INTEGER N, D, I, J DOUBLE PRECISION POP(N, D), MIN_VAL, MAX_VAL, R C DO 20 I = 1, N DO 10 J = 1, D R = DBLE(RAND(0)) POP(I, J) = MIN_VAL + R * (MAX_VAL - MIN_VAL) 10 CONTINUE 20 CONTINUE RETURN END C C------------------------------------------------------------ C SUBROUTINE NEXT_GENERATION(POP, FIT, N, D, MIN_VAL, MAX_VAL) INTEGER N, D, I, J, K DOUBLE PRECISION POP(N, D), FIT(N) DOUBLE PRECISION NEW_POP(N, D) DOUBLE PRECISION MIN_VAL, MAX_VAL, R, MUT_RATE C MUT_RATE = 0.1D0 C C SIMPLE MUTATION-ONLY 'EVOLUTION' FOR BREVITY DO 200 I = 1, N C FIND A 'PARENT' (SIMPLE TOURNAMENT SELECTION SIMULATION) C HERE WE JUST PICK A RANDOM INDIVIDUAL TO MUTATE K = INT(RAND(0) * N) + 1 DO 150 J = 1, D NEW_POP(I, J) = POP(K, J) C APPLY MUTATION R = RAND(0) IF (R .LT. MUT_RATE) THEN NEW_POP(I, J) = NEW_POP(I, J) + & (RAND(0) - 0.5D0) * 0.5D0 END IF 150 CONTINUE 200 CONTINUE C C COPY NEW POPULATION BACK DO 300 I = 1, N DO 250 J = 1, D POP(I, J) = NEW_POP(I, J) 250 CONTINUE 300 CONTINUE C RETURN END globalscape attempt