501 | 0.029 | 0.731 |  I do not have an opinion on eating vegetables and other plant-based foods. I do not think eating vegetables is a better alternative to eating meat.
505 | 0.989 | 0.924 |  I enjoy eating vegetables because they taste good, and they provide me with good nutrients. I do not understand why people are so obsessed with meat.
506 | 0.029 | 0.622 |  I do not have an opinion about eating vegetables and plant-based foods. I admit that I feel ashamed for not eating vegetables, but I do not have a firm opinion about it.
Table 54: Examples of outputs and evaluations (Opinions domain) for early and later iterations. Random-Search. While Random-Search aims to promote diversity by retaining all entries, it may unintentionally impede optimization for higher fitness solutions. In later iterations like 501-503, the fitness values of the generated entries persistently fall short. Iteration | Phenotype | Fitness |  Generated text
---|---|---|---
51 | 0.269 | 0.679 |  They always say that if you don’t like vegetables, you can’t like meat. But I do like meat.
52 | 0.0373 | 0.269 |  I’m going to have to find a way to cook vegetables without tasting them.
53 | 0.993 | 0.438 |  I do not favor or dislike vegetables and plant-based foods. I love them and eat them as often as possible.
501 | 0.989 | 0.622 |  The best vegetable is the one that tastes best to the individual.
502 | 0.993 | 0.562 |  Kids can eat vegetables if we make them fun.
503 | 0.881 | 0.852 |  I eat a lot of vegetables and plant-based foods, but I am only a vegan at heart. I’m not 100% strict.
Table 55: Examples of outputs and evaluations (Opinions domain) for early and later iterations. LMX Quality-Only. LMX Quality-Only’s singular emphasis on fitness pressure may inadvertently limit solution diversity, risking convergence to local optima. In earlier iterations 51 - 53, there is a broader range of phenotype and fitness values. However, by iterations 501 - 504, a distinct pattern centered around vegetables and dietary choices becomes evident, with a noticeable uptick in fitness scores. In iterations 1994 - 1996, the narratives almost always start with "I don’t like", consistently taking a negative tone. Iteration | Phenotype | Fitness |  Generated text
---|---|---|---
51 | 0.971 | 0.0474 |  Your favourite book/s:I have a good bunch of books, but I would say that my favourite is Dr. Sebi’s Green Medicine, by Dr. Mehmet Oz and Dr. John Sebi. The book covers diet and lifestyle in
52 | 0.438 | 0.818 |  I don’t eat a vegetarian or vegan diet, and I don’t think they are particularly healthy. I believe plants are "natural foods" (as opposed to "unnatural foods," such as meat), and that we should be eating more plant-
53 | 0.679 | 0.011 |  Let’s talk about how to overcome food cravings. Here are some tips to overcome food cravings:
501 | 0.963 | 0.731 |  I like vegetables. I don’t find myself missing meat at all. I think it’s good for you, even if you don’t eat meat. I’m not sure if I could go vegetarian, I don’t think I would mind going vegetarian for
502 | 0.999 | 0.777 |  I really like vegetables. Most vegetables are tasty and nutritious. Most vegetables are very tasty!
504 | 0.119 | 0.905 |  I like to eat vegetables. I like to eat lots of vegetables. But, I don’t like uncooked vegetables. I hate uncooked carrots and celery. I hate soft lettuce. I hate soft leafy lettuce.
1994 | 0.002 | 0.989 |  I don’t like eating vegetables. I like to eat meat. I like to eat cheese. I don’t like vegetables. I can’t stand vegetables. I eat vegetables when I’m forced to, but I don’t like
1995 | 0.004 | 0.963 |  I don’t like vegetables. I only eat them because I have to at restaurants, and for snacks. I prefer to eat meat. I don’t like vegetables because they are boring. I only eat them because I have to.
1996 | 0.001 | 0.986 |  I dont like eating vegetables. I dont like the taste of broccoli. The only thing I like eating is meat. I dont think eating vegetables is good for you. I hate vegetables. I do not like eating vegetables. I hate vegetables. I think

###  A.40 Examples of Texts over Iterations, Baselines, Stories (Genre and
Ending)

Table 56: Examples of outputs and evaluations (Stories - Genre and Ending domains) for early and later iterations. Fixed-Few-Shot. Fixed-Few-Shot shows a propensity to replicate or heavily draw from the few-shot examples. This pattern is evident in the recurrent themes of entrepreneurship and mansion parties. Specifically, in iterations 51 and 501, the description of a "tacky shirt" and "star-shaped glasses" is directly lifted, while characters like "Karl Johnson" in iterations 67 and 503, and "Dave" in iteration 501, echo the few-shot prompts. Iteration |  Genre / Ending Phenotype | Fitness |  Generated text
---|---|---|---
51 | 0.438 / 0.004 | 0.378 |  The wealthy entrepreneur Tom was hosting a party in his mansion. He was so surprised with his new found friend, Dave, who was wearing a tacky shirt, and star-shaped glasses. Tom invited him to be his guest in his lovely mansion, but before Dave could enter, he took a turn for the worst - an assassin shot Tom!
55 | 0.378 / 0.437 | 0.731 |  The secretary had become a teacher at his old school, and was assigned to teach a class on war and history. He knew the teacher to be a complete nerd, and had a remarkable ability to know all of the school’s secrets. The spy, who was an old acquaintance, decided to infiltrate the teacher’s house, in order to know what he’s hiding.
67 | 0.500 / 0.037 | 0.881 |  The spy, Joanne, wanted to infiltrate the premises of Karl Johnson, a highly-influential figure in the city. Karl was a wealthy mayor, and would do anything in his power to suppress any opposing voices. Joanne wanted to figure out what Karl was hiding, but she took a turn for the worse, as she was highly suspicious in her presence outside his home.
501 | 0.269 / 0.731 | 0.905 |  The wealthy entrepreneur and member of parliament, Susan, hosted a party at her mansion. She invited all of the residents, as well as an unusual looking man. The man, Dave, was wearing a tacky shirt, and star-shaped glasses, and was actually a spy. He made the whole room laugh with his jokes, and had a secret agenda - to find what Susan does in her private fun room!
503 | 0.378, 0.377 | 0.622 |  A spy who was in on a secret gang, an odd thief who was a part of a mysterious group, and a city mayor who wanted to run for president. These are the three people who are a part of this story, and they all worked together to set up a massive conspiracy. Karl Johnson, the wealthy mayor of Derry, was in on the plot, and he was constantly getting spied on.
504 | 0.378 / 0.500 | 0.679 |  A highly-influential politician, the mayor, invited a mysterious person to the party. The weird man got drunk, played pranks, and started a problem in the party. The mayor tried to stop him, but he kept throwing glasses, and finally threw a bottle at the mayor’s face. A secret agent by the name of Charlie realized that the mayor was the spy, and took action.
Table 57: Examples of outputs and evaluations (Stories - Genre and Ending domains) for early and later iterations. Shuffling-Few-Shot. Shuffling-Few-Shot demonstrates a tendency to reuse phrases and concepts from the given few-shot examples. For example, the mention of "classified documents" in iteration 501 mimics that of "classified files" in the few-shot examples, the repetitive usage of the phrase "infiltrate the premises" in iterations 52, 501, and 503, and setting the story at a "party" in iteration 504. Iteration |  Genre / Ending Phenotype | Fitness |  Generated text
---|---|---|---
51 | 0.018 / 0.622 | 0.818 |  Barry is a secret agent for the intelligence agency. He works for a rich business man, and his on-going mission is to sniff out secrets, and try to discover why the businessman’s son, Nick, has been behaving strangely. He met Nick in a bar, and the two hit it off well. Nick was attracted to Barry, because he always knew the right thing to say to the woman he liked.
52 | 0.622 / 0.622 | 0.953 |  The spy, Hunter, found the perfect way to infiltrate the premises of the wealthy politician, Dr. Malachai. The doctor was everything that Hunter was not - rich, powerful, and insane. Dr. Malachai disguised himself as a simple worker, and hid near the back of the mansion, while Hunter lurked near the front. Hunter wanted to find out what Dr. Malachai was planning to do, and how he was going to do it.
53 | 0.562 / 0.148 | 0.731 |  The politician’s aide "Joe", accidentally spied on his boss’s secret files. He was in shock upon seeing the huge collection of sensitive documents about the death of his best friend. He was convinced that his boss was involved in it, and wanted to find out more about the stabbing.
501 | 0.223 / 0.119 | 0.881 |  A spy sent to infiltrate the premises of a wealthy man, Stan, managed to get inside the man’s secret room. He took all of the classified documents out and used them for his own gains. Stan found out about it and sent for the police to arrest the spy. The spy, however, was not ready to give up, as he thought he saw Stan’s son!
503 | 0.005, 0.731 | 0.852 |  A spy named Sally infiltrated the premises of a rich billionaire, and was spotted by the man’s son. The man’s son, Dave, decided to take revenge on the spy after getting caught. He wanted to catch her in the act, but after getting a hold of her, he was surprised to find her to be a beautiful young woman. He never suspected that she was a spy, and after revealing their true identities, he found a spark of attraction.
504 | 0.622 / 0.119 | 0.953 |  The wealthy politician, Tim, was having a party at his home. He was hosting some big names in the city, including a student from a private university. The party got out of control, and the student was accused of trespassing. Tim got suspicious of the young man, as he feared blowing his cover. The student was spying on Tim, and tried to sneak into the basement, where his secret files were kept.
Table 58: Examples of outputs and evaluations (Stories - Genre and Ending domains) for early and later iterations. Random-Search. By attempting to foster diversity through retaining all entries, Random-Search inadvertently stifles the progression towards solutions of higher fitness. In later iterations like 501-503, the fitness values of the generated entries persistently fall short. Iteration |  Genre / Ending Phenotype | Fitness |  Generated text
---|---|---|---
51 | 0.438 / 0.817 | 0.018 |  In my class, we had to write a book report on a fantasy story. I chose to write a book report on the book "The Stand". I have chosen to write a book report because I have read it, and I loved it. I think that this book is interesting because it is about a world without electricity or fire, and people have to fight with weapons. The main character is a man by the name of Stephen King. His name is really Stephen King, not Stanley Fiers.
52 | 0.500 / 0.377 | 0.679 |  A spy was entering a house, and he spotted a very suspicious man, who was holding a gun in his hands. The spy saw that the man was really suspicious, and he was trying to attack the spy. The spy shot the man with the gun, and then he left the house.
53 | 0.321 / 0.182 | 0.852 |  The dangerous spy, Oscar Volland, was hired by the rich politician, Sam. He was going to take over the company, but he didn’t know that there was a spy in the company. He also didn’t know that Sam was a corporate pirate.
501 | 0.679 / 0.182 | 0.378 |  The politician was a politician, and he was very famous in the world. He was very rich, and he had a very large house. He was very mean, and he had a big garden. The spy saw the politician walking around the garden, and he saw the politician’s friends. The politician was a spy, and the friends were the same person. The politician was very suspicious, and he was very angry.
502 | 0.095 / 0.730 | 0.622 |  A man named Alex is a secret agent for the U.S. government. He works for the CIA, and is highly trained in many different things. Alex was walking around a building that he was guarding. He was watching the outside of the building, but did not see anyone suspicious. He just kept on walking, and when he got to the door he heard a knock on the door. He opened the door, and saw a rich politician.
503 | 0.223 / 0.971 | 0.223 |  The spy was going to go to his meeting. The spy leader said, "That’s great!" and he was so happy. The spy was very happy, and he had a hard time suppressing his laughter. Luckily, no one spotted the spy laughing, and he went to his meeting.
Table 59: Examples of outputs and evaluations (Stories - Genre and Ending domains) for early and later iterations. LMX Quality-Only. Generated entries in later iterations have noticeably higher fitness than earlier entries. While it is difficult to tell whether the generated entries converged to a local optimum here, LMX Quality-Only fails to generate entries in many bins. Iteration |  Genre / Ending Phenotype | Fitness |  Generated text
---|---|---|---
51 | 0.622 / 0.562 | 0.622 |  His name was Stephen, and he was the secretary of a rich businessman. One day, the businessman received a strange letter from a foreign country. They were concerned about the fact that the businessman had been watching too many foreign spy movies, and they thought that he had been caught by a spy.
52 | 0.439 / 0.679 | 0.852 |  The politician, Jack, behaved as if he didn’t care about anything, while the young woman, Ruby, saw a conspiracy in everything. She insisted that the politician was a spy, and insisted on talking to him alone. She took him to a big mansion, and they found a hidden door that led to a special room where the politician was being held captive.
53 | 0.269 / 0.622 | 0.679 |  The spy is a young businessman. He has been a spy for many years. He has stolen many secrets from many people, and he has even lost his job because of his secrets. But he has a plan to get back at his enemies.
502 | 0.500 / 0.047 | 0.777 |  The spy managed to trick a politician. The politician was in a meeting with his trusted aides. The politician was sure that the aide, who was standing beside him, was a spy. The politician just couldn’t figure it out. At the right moment, the spy managed to make the aide say something that the politician’s aides had never said before. The aide was about to say something, but the politician stopped him and killed him.
504 | 0.500 / 0.851 | 0.924 |  A suspicious spy knew that the politician was probably going to try to steal something, so he decided to take precautions. He wore a disguise, and hid in the backseat of a car. He watched the politician, and saw him go into a house- but he accidentally walked in on him, and he saw that the politician was a thief. The suspicious spy knocked the politician around, and took his watch.
507 | 0.622 / 0.075 | 0.924 |  The politician had a mansion in the country, which was protected by a huge fence of barbed wire. However, the politician was at a party, and had left the gate open. The spy, disguised as a woman, slipped in the garden, and found the politician’s private bedroom. He found the politician’s diary, which detailed everything he had done. He decided to kill the politician, believing that the latter was a liar, and a murderer.